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Estimating the Force of Infection for Dengue Virus Using Repeated Serosurveys, Ouagadougou, Burkina Faso
research-article
Author(s):
Jacqueline K. Lim
,
Mabel Carabali ,
Tansy Edwards ,
Ahmed Barro ,
Jung-Seok Lee ,
Desire Dahourou ,
Kang Sung Lee ,
Teguewende Nikiema ,
Mee Young Shin ,
Emmanuel Bonnet ,
Therese Kagone ,
Losseni Kaba ,
Suk Namkung ,
Paul-André Somé ,
Jae Seung Yang ,
Valéry Ridde ,
In-Kyu Yoon ,
Neal Alexander ,
Yaro Seydou
Publication date (Print):
January 2021
Journal:
Emerging Infectious Diseases
Publisher:
Centers for Disease Control and Prevention
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Abstract
Because of limited data on dengue virus in Burkina Faso, we conducted 4 consecutive age-stratified longitudinal serologic surveys, ≈6 months apart, among persons 1–55 years of age, during June 2015–March 2017, which included a 2016 outbreak. The seroconversion rate before the serosurvey enrollment was estimated by binomial regression, taking age as the duration of exposure, and assuming constant force of infection (FOI) over age and calendar time. We calculated FOI between consecutive surveys and rate ratios for potentially associated characteristics based on seroconversion using the duration of intervals. Among 2,897 persons at enrollment, 66.3% were IgG-positive, and estimated annual FOI was 5.95%. Of 1,269 enrollees participating in all 4 serosurveys, 438 were IgG-negative at enrollment. The annualized FOI ranged from 10% to 20% (during the 2016 outbreak). Overall, we observed high FOI for dengue. These results could support decision-making about control and preventive measures for dengue.
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Most cited references16
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The global distribution and burden of dengue
Samir Bhatt, Peter W. Gething, Oliver J Brady … (2013)
Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes 1 . For some patients dengue is a life-threatening illness 2 . There are currently no licensed vaccines or specific therapeutics, and substantial vector control efforts have not stopped its rapid emergence and global spread 3 . The contemporary worldwide distribution of the risk of dengue virus infection 4 and its public health burden are poorly known 2,5 . Here we undertake an exhaustive assembly of known records of dengue occurrence worldwide, and use a formal modelling framework to map the global distribution of dengue risk. We then pair the resulting risk map with detailed longitudinal information from dengue cohort studies and population surfaces to infer the public health burden of dengue in 2010. We predict dengue to be ubiquitous throughout the tropics, with local spatial variations in risk influenced strongly by rainfall, temperature and the degree of urbanisation. Using cartographic approaches, we estimate there to be 390 million (95 percent credible interval 284-528) dengue infections per year, of which 96 million (67-136) manifest apparently (any level of clinical or sub-clinical severity). This infection total is more than three times the dengue burden estimate of the World Health Organization 2 . Stratification of our estimates by country allows comparison with national dengue reporting, after taking into account the probability of an apparent infection being formally reported. The most notable differences are discussed. These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue. We anticipate that they will provide a starting point for a wider discussion about the global impact of this disease and will help guide improvements in disease control strategies using vaccine, drug and vector control methods and in their economic evaluation. [285]
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Refining the Global Spatial Limits of Dengue Virus Transmission by Evidence-Based Consensus
Oliver J Brady, Peter W. Gething, Samir Bhatt … (2012)
Introduction Despite increased interest in dengue in recent years, the global distribution of dengue remains highly uncertain. Estimates for the population at risk range from 30% [1] to 54.7% [2] of the world's population (2.05–3.74 billion) while the Centers for Disease Control (CDC) and the World Health Organization (WHO) currently disagree on dengue presence in 34 countries across five continents (Table S1). Clinical features of dengue virus infection include fever, rash and joint pain [3], which ensure the disease's misdiagnosis and mis-reporting among many other febrile illnesses. The diagnostic methods available also have limitations and a full complement of tests is not feasible in many healthcare settings. There is consensus, however, that dengue is a growing problem both geographically and in its intensity [4], [5], [6]. There is an urgent need to compile more extensive occurrence records of dengue virus transmission and assess them for contemporariness and accuracy. Evidence on dengue transmission comes in a wide variety of forms, with varying levels of spatial coverage and reliability. A global audit of dengue distribution therefore requires a transparent methodology to compile these disparate data types and synthesise an output map summarising the current consensus for each country. Such a methodology for compiling and assessing evidence must be robust, repeatable, able to evaluate a large variety of evidence types and incorporate expert opinion. An ideal output metric is a summary statistic (hereafter referred to as evidence consensus) that quantifies certainty on dengue virus transmission presence or absence given the accuracy and contemporariness of the evidence available. An evidence-based map of the current distribution of dengue virus transmission will have direct implications for design and implementation of dengue surveillance and, by showing gaps in contemporary knowledge, provide an advocacy platform for improved data. Existing approaches to mapping the global limits of vector-borne diseases have used estimates of biological suitability of local environments, which have proved informative in the cases of some pathogens, such as Plasmodium falciparum [7], [8] and P. vivax [9]. Several approaches have been used to map biological suitability for dengue using non-dengue-specific variables such as temperature, rainfall and satellite-derived environmental variables [1], [10], [11]. Although successive attempts have each increased predictive capacity and resolution, this approach produces variable results in Africa due to a scarcity of confirmed occurrence points across extensive geographic areas. An alternative approach has been to map evidence of dengue occurrence making no assumptions about biological suitability, as in Van Kleef et al., who reviewed published literature to contrast historic, current and future limits of dengue [5]. To date dengue mapping has focussed on future scenarios, yet understanding of the current distribution of dengue virus transmission is far from complete and needs to be better evaluated before we can make predictions about forthcoming patterns and trends. In this study we combine evidence from large occurrence-point style databases used in biological suitability mapping approaches with a wider systematic review of various sources of evidence to create a more comprehensive dengue database. Using this database we then use the novel method of defining evidence consensus to evaluate the current level of certainty on dengue virus transmission presence or absence at national (and some sub-national) levels using a weighted evidence scoring system. Finally, we present these results as a series of global maps that explicitly identify surveillance gaps. This study is the initial part of a five year project to collect, analyse and publicise global dengue virus transmission data. While the map presented here is the most extensive display of current dengue evidence available, we hope that continual data acquisition will result in more evidence from uncertain areas, increasing the resolution at which we can map evidence consensus in future advances. Methods Collection of dengue virus transmission evidence Evidence for indigenous dengue virus transmission was obtained from four evidence categories: health organisations, peer-reviewed evidence, case data and supplementary evidence (Figure 1). The first three categories were used for all countries. For countries where some of these categories were not available and/or did not provide good consensus, the fourth category of supplementary evidence was used. Evidence was initially collected at a country level (Admin0), but resolution was improved to a state/province level (Admin1) or district level (Admin2) at the fringes of the distribution of detectable virus transmission when sufficient data were available. 10.1371/journal.pntd.0001760.g001 Figure 1 Schematic overview of the methods. Blue diamonds describe input data; orange boxes denote experimental procedures; green ovals indicate output data; dashed lines represent intermediate outputs and solid lines final outputs; dotted white ovals denote the number of countries for which data was available and added to the final output. Dotted rectangles identify the different evidence categories and their main data sources. S1 = Protocol S1. Country dengue status as defined by health organisations was determined by consulting the WHO [12] and CDC [13] dengue distribution maps as well as the Global Infectious Diseases and Epidemiology Online Network (GIDEON) database [14]. GIDEON provides a collection of literature and case reports for a range of tropical and infectious diseases in 224 countries. Dengue status by country was recorded as present or absent. The peer-reviewed evidence category contained evidence of dengue occurrence as determined by peer-reviewed sources where details of diagnostic techniques were given. Peer-reviewed journal (Google Scholar, PubMed, ISI Web of Science) and disease surveillance network (ProMED archives, Eurosurveillance archives) searches were conducted with search terms “country” or “Admin1/2” and “dengue”. Sources were included for the period 1960–2012 and only if cases were confirmed as resulting from indigenous (i.e. not imported) transmission. The specialist regional journal collections African Journals Online (http://www.ajol.info/) and China National Knowledge Infrastructure (http://en.cnki.com.cn/) were also searched. Extra publications were found by searching using the location term in Genbank nucleotide records for dengue viruses isolated from human hosts. The search of peer-reviewed sources of evidence resulted in a total of 285 articles being selected for 123 countries where positive dengue occurrence records were identified. This included evidence from returning travellers who were diagnosed upon return to their often non-endemic home countries as opposed to the transmission setting. For these cases, evidence was attributed to the place to which they had travelled. The added value of returning traveller reports is that the travellers are often more immunologically naïve to dengue infections, and also that diagnosis is often pursued more rigorously. Therefore, the sensitivity of detecting an infection is increased. The results of our search were then cross-referenced against a dengue occurrence-point database compiled internally, in a separate exercise. Unlike our country-specific searches, this database of 2836 articles results from searches simply for “dengue”, which were then geo-referenced using the article text. Full details are available in Protocol S1 and the geographic location of the occurrence points are displayed in Figures S1, S2, S3, S4, S5, S6. This cross-referencing resulted in the inclusion of an additional 16 articles in the current analysis and also provided increased justification for our choice of countries to evaluate at Admin1 level. The case data category contained evidence of dengue outbreaks (minimum 50 infections) where evidence contained less diagnostic detail, but was more informative about the magnitude of dengue transmission occurring. Case data from the most recent outbreak were obtained from the Program for Monitoring Emerging Diseases (ProMED) archive search, WHO DengueNet data query [15] and from GIDEON which holds a detailed record of government-reported case numbers. This resulted in 100 countries with useful dengue case data. In many resource-poor countries, both surveillance and researcher-generated reports are rare. Therefore, in countries where other evidence categories were sparse, we looked for supplemental evidence that suggested possible dengue virus presence. Supplemental evidence types included: presence of an established mosquito vector population of public health significance (Aedes aegypti, Ae. albopictus or Ae. polynesiensis) as documented by peer-reviewed literature, confirmed presence of multiple other rarely diagnosed arboviral diseases as documented by peer-reviewed literature, news reports of dengue epidemics found using GoogleNews archives (http://news.google.co.uk/archivesearch) and travel advisories from the National Travel Health Network and Centre (http://www.nathnac.org/ds/map_world.aspx) issued at a country-level. We included evidence of multiple other rarely diagnosed arboviral diseases, as these are informative about the ability of a country to detect any possible dengue infection. If other arboviral diseases are poorly reported, but documented by peer-reviewed literature as present, then it is possible that dengue is also underreported. In addition to this, we cross-referenced our dataset with the HealthMap database (www.healthmap.org/dengue/). This website-based application automatically geo-positions cases from websites with news reports and outbreak alerts related to dengue and contains data from a wide variety of sources dating back to 2007 [16], [17]. This extensive database contributed important evidence especially at smaller spatial scales and in areas where translated articles are not so easily obtained. Supplementary evidence was used in evaluating dengue consensus in 45 countries. While the categories are clearly defined here and in Figure 1, some overlap of evidence sources did occur, depending on the information content of each source. This meant evidence sources such as ProMED reports could be included twice, in both the peer-reviewed evidence and case data categories, if they contained information about diagnostic tests used for confirmation as well as overall outbreak case numbers. In this section we outline the main sources used for each category, but it should be noted that if evidence from a particular source fitted the criteria for a different evidence category, it was not excluded, but rather included in that category. Quantifying evidence with a weighted scoring system In order to quantify evidence consensus, a weighted scoring system was developed that attributed positive values to evidence of presence and negative values to evidence of a lack of presence. The aim here was to use an optimal subset of evidence to accurately assess dengue status within a given area. By scoring the evidence categories mentioned above individually and then combining their respective scores, we were able to calculate “evidence consensus,” a measure of how strongly the combined evidence collection supports a dengue-present or dengue-absent status (Figure 2). We defined a country as having “complete consensus” on dengue presence when the evidence base was comprised of contemporary forms of most or all of the following evidence types: 1) unanimous health organisations agreement, 2) a seroprevalence survey, 3) Polymerase Chain Reaction (PCR) typing of dengue virus or dengue viral RNA, 4) a foreign visitor to the area with a confirmed dengue infection upon returning to their home country, and 5) records of an epidemic of greater than 50 infections. Such a country has a consensus score of between 80% and 100%. A country with a complete consensus on dengue virus absence is characterised by all health organisations agreeing on dengue absence and high healthcare expenditure (as an approximate proxy for surveillance capability), therefore accounting for both the observed absence of dengue and the minimised possibility of any undetected dengue infections. Such a country scores between −80% and −100% on our scale. A country with no consensus on dengue virus status is characterised by conflicting evidence from different categories and scores close to 0%. Each evidence category was scored independently and category weights applied to reflect the level of detail each category provides: health organisation status (maximum score 6), peer-reviewed evidence (maximum 9), case data (maximum 9) and supplementary evidence (maximum 6). To support the choice of assigned category weights we performed a sensitivity analysis in which two alternative evidence weighting scenarios were applied to the same sources of data: 1) neutral (all categories hold the same weight) and 2) reversed (health organisation status and supplementary evidence hold weight 9, peer-reviewed evidence and case data hold weight 6). We then checked for any major deviations in overall country score resulting from such alternative scenarios. 10.1371/journal.pntd.0001760.g002 Figure 2 Overview of the evidence scoring system. Cream boxes represent mandatory categories while red boxes represent optional categories that are only used where required (see Methods). Dashed lines surround individual parameters that are assessed and totalled in the scoring system. Green boxes describe the level of evidence, with a given score in the blue oval. * Each individual piece of literary evidence is scored for contemporariness and accuracy before taking an average of the whole set then adding the combination score. Evidence consensus is calculated as the proportion of the maximum possible score from the dashed lined characteristics that are used. Δ Maximum possible score depends on which categories are included and can vary from 15 (Case data and Health organisation status, but no peer-reviewed evidence available) to 30 (all evidence categories included). Yrs = years. HE = total healthcare expenditure per capita at average U.S. $ exchange rates. Health organisation evidence The data from the three health organisations (WHO, CDC and GIDEON) comprised discrete presence or absence answers. A consensus (+++ or −−−) scored 6 or −6 respectively, while a lack of consensus (++− or −−+) scored 3 or −3 respectively (Figure 2A). This gave a maximum score for this category of ±6. Peer-reviewed evidence and returning traveller reports These forms of evidence were each scored independently for contemporariness and accuracy. The date of occurrence was used for scoring as follows: between 2012–2005 = 3, 2004–1997 = 2 and pre-1997 = 1 (Figure 2B). This corresponded to a conservative estimate of the inter-epidemic period for dengue of three to five years [18]. This score was then added to a score for accuracy, whereby high accuracy, and a score of 3, was characterised by PCR methods, a Plaque Reduction Neutralization Test (PRNT), or a detailed case description of a complication of the disease. Complications of the disease were either dengue haemorrhagic fever (DHF) grades 1 and 2 or dengue shock syndrome (DSS) grades 3 and 4 under the old classification scheme [19] or severe dengue under the new classification scheme [3]. Medium accuracy methods including IgM- and IgG- based ELISA and Hemagglutination Inhibition (HI) assay approaches scored 2 because their calibration is sensitive to background immune responses [20], antibody response is variable over the course of an infection [21] and the test can cross-react with other non-dengue arboviruses [20]. A low accuracy score of 1 was used for articles that only reported case numbers with a non-dengue-specific case definition or a low participant number. Each included article was scored separately and then an average score was taken from all articles. This presented the possibility of devaluing the score of the most accurate and contemporary piece of evidence, so an extra score was added to reflect increased certainty provided by multiple forms of evidence. Evidence types 2) through 5) described above contributed to this extra score as such: if two types of evidence were present a score of 1 was added, three types = 2, four types = 3. This resulted in a maximum available score of 9 for peer-reviewed evidence. Case data This category was scored by contemporariness in eight-year intervals. The most frequent year in which an outbreak (over 50 cases or over 15 cases if the population is below 100,000) occurred was again scored in average inter-epidemic period intervals: 0–7 years since the last outbreak scored 9, 7–14 years = 6, 14–21 = 3, 21–28 = −3, 28–35 = −6, 35+ = −9 (Figure 2C). Where case data were unavailable, the distinction between true absences and inadequate surveillance was made using total annual healthcare expenditure (HE) per capita at average U.S. Dollar exchange rates (2011 WHO health statistics) [22]. Higher HE has been linked to better overall public health infrastructure, which includes high-quality diagnostic resources, greater healthcare coverage and higher levels of expertise, all of which may result in a more thorough characterisation of dengue status at the country-level [23], [24], [25]. Therefore, the lower the HE, the less certain we can be that an absence of case data accurately reflects an absence of dengue transmission. Class intervals for HE were chosen to reflect regional differences both within and between continents. Where information on HE was unavailable (Somalia, North Korea and Zimbabwe), low HE status was assigned. All overseas territories were assumed to have the same HE as their parent nations. The following criteria were used to derive the case scores in the absence of dengue case data: HE<$100 and reports of sporadic unconfirmed cases gave a score of 6, HE<$100 = low HE = 3, $100≤HE<$500 = medium HE = −3, HE≥$500 = high HE = −9 (Figure 2C). The maximum score for the case data category was ±9. Supplementary evidence This formed part of the evidence base if there was some suggestion of dengue presence, but the above three categories were insufficient to provide certainty on dengue status. If only two evidence types were available (see above), a score of 2 was given, three types = 4, four types = 6 (Figure 2D). Supplementary evidence carried a maximum score of 6. Where a national score showed some uncertainty and an additional factor existed that was not captured by the default scoring system, an adjustment of up to ±3 was applied. For example, if multiple evidence categories suggested dengue presence in a country with high HE, but there was no case data, then the case data score was adjusted so as not to hold a disproportionate weight in deciding overall dengue status. This is termed the “ad hoc adjustment” (Figure 2E). To derive an overall country evidence consensus score, the scores for all evidence categories were summed, and then divided by the maximum possible score and multiplied by 100. Evidence consensus was then mapped according to nine equal interval categories from 100% to −100% that differentiated evidence consensus worldwide, with evidence consensus being defined as complete (±79% to ±100%), good (±57% to ±78%), moderate (±34% to ±56%), poor (±12% to 33%) or indeterminate (−11% to 11%). An odd number of intervals was chosen so as to highlight places where consensus is very low (indeterminate) and where improved surveillance is particularly needed. As such, the resulting classification of consensus scores should not be strictly interpreted, but rather taken as a general indication of the quality of dengue evidence in a given location. A full breakdown of the exact evidence included, individual scores and overall consensus percentages are given for each country in Table S1 and Figure S7. Refining the evidence base and map with questionnaires targeted to consensus poor countries In countries where evidence consensus was at best moderate, we attempted to increase consensus through targeted questionnaires. The questionnaire asked about endogenous surveillance and data collection. If available, diagnostic method(s) and summary results were requested. Any returned data or reports were then entered into their relevant evidence categories and scored in combination with existing evidence. Questionnaires were distributed to healthcare officials in the country of interest as well as selected offices of the Institut Pasteur. Questionnaire responses and expert comments are part of an on-going process that will lead to future modifications of this map. Identification of countries that publically distribute dengue case data To map public awareness of dengue worldwide, we searched the ministry of health websites of each of the 128 countries identified as dengue-present (evidence consensus positive but not indeterminate). A country was indicated as publicly displaying dengue data if national dengue case numbers were displayed annually or during epidemic years at a minimum. Population at risk calculations To calculate the maximum possible population at risk for dengue virus transmission we obtained total population counts from the Global Rural Urban Mapping Project (GRUMP) for the 128 countries identified as dengue-present. The GRUMP beta version provides gridded population count estimates at a 1×1 km spatial resolution for the year 2000 [26], [27]. Population counts for the year 2000 were projected to 2010 by applying country-specific urban and rural national growth rates [28] using methods described previously [29]. As 2010 forms a landmark year for many national censuses, we were able to adjust these expanded population counts using the United Nations 2010 population estimates [30]. Results Global distribution of dengue virus transmission based on evidence consensus The global distribution of dengue virus transmission as defined by evidence consensus is shown in Figures 3–7. The mapped colour scale ranges from complete consensus on dengue presence (dark red) to indeterminate consensus on dengue status (yellow) then through to complete consensus on dengue absence (dark green). A full list of the evidence used for each area and their scoring is available in Table S1 and Figure S7. In total we identified 128 countries as dengue-present (i.e. positive values outside the indeterminate range), compared to 100 from the WHO, 104 from the CDC and 118 from GIDEON. Compared to the lists produced by the WHO and CDC, we identified 41 additional countries where evidence consensus for presence was outside the indeterminate range yet dengue-absent status was assigned by at least one of these health organisations. 10.1371/journal.pntd.0001760.g003 Figure 3 Evidence consensus on dengue virus presence and absence in the Americas. Figure 3 shows the areas categorised as complete evidence consensus on dengue absence in dark green, through to areas with indeterminate evidence consensus on dengue status in yellow, then up to areas with complete evidence consensus on dengue presence in dark red. Stars indicate one off indigenous transmission events with fewer than 50 cases. The map displays evidence consensus at Admin1 (state) level for Argentina and Uruguay, Admin2 (county) level for the United States of America and Admin0 (country) level for all other countries. 10.1371/journal.pntd.0001760.g004 Figure 4 Evidence consensus on dengue virus presence and absence in Africa. Figure 4 shows the areas categorised as complete evidence consensus on dengue absence in dark green, through to areas with indeterminate evidence consensus on dengue status in yellow, then up to areas with complete evidence consensus on dengue presence in dark red. Stars indicate one off indigenous transmission events with fewer than 50 cases. The map displays evidence consensus at Admin1 (state) level for Saudi Arabia and Pakistan and Admin0 (country) level for all other countries. 10.1371/journal.pntd.0001760.g005 Figure 5 Evidence consensus on dengue virus presence and absence in Asia. Figure 5 shows the areas categorised as complete evidence consensus on dengue absence in dark green, through to areas with indeterminate evidence consensus on dengue status in yellow, then up to areas with complete evidence consensus on dengue presence in dark red. Stars indicate one off indigenous transmission events with fewer than 50 cases. The map displays evidence consensus at Admin1 (state) level for Saudi Arabia, Pakistan, India, China and South Korea and Admin0 (country) level for all other countries. 10.1371/journal.pntd.0001760.g006 Figure 6 Evidence consensus on dengue virus presence and absence in Europe. Figure 6 shows the areas categorised as complete evidence consensus on dengue absence in dark green, through to areas with indeterminate evidence consensus on dengue status in yellow. Stars indicate one off indigenous transmission events with fewer than 50 cases. The map displays evidence consensus at Admin2 (county) level for France and Croatia and Admin0 (country) level for all other countries. 10.1371/journal.pntd.0001760.g007 Figure 7 Evidence consensus on dengue virus presence and absence in Australasia. Figure 7 shows the areas categorised as complete evidence consensus on dengue absence in dark green, through to areas with indeterminate evidence consensus on dengue status in yellow, then up to areas with complete evidence consensus on dengue presence in dark red. Stars indicate one off indigenous transmission events with fewer than 50 cases. The map displays evidence consensus at Admin1 (state) level China, Admin2 (county) level for Australia and Admin0 (country) level for all other countries. Even after performing the sensitivity analysis described earlier, the number of countries defined by our methodology as dengue-present but defined by WHO/CDC as absent never dropped below 36 (Table 1). We therefore suggest that this list of 36 countries be subject to a review regarding their current health organisation dengue-absent classification. Of these countries, 31 had at least moderate consensus on dengue presence in our final analysis. 10.1371/journal.pntd.0001760.t001 Table 1 Countries that require a reassessment of dengue status by health organisations. Country Evidence consensus (%) Health organisations with dengue-absent status Evidence included American Samoa Good (76) CDC 2007 outbreak and SE Aruba Good (67) WHO 2005 outbreak and PCR virus typing Bahamas Good (67) WHO 2011 outbreak Benin Moderate (40) WHO, CDC Returning traveller reports, PCR virus typing and SE Brunei Good (75) WHO 2010 outbreak, PCR virus typing Cameroon Good (76) WHO Seroprevalence surveys, returning traveller reports and questionnaire responzse Cayman Islands Good (69) WHO 2010 outbreak and SE Chad Moderate (40) WHO, CDC Returning traveller reports and SE Comoros Complete (81) WHO 2010 outbreak, seroprevalence survey and returning traveller reports Cook Islands Good (60) WHO, CDC 2009 outbreak, PCR virus typing and SE Djibouti Good (75) WHO 2005 outbreak, returning traveller reports and PCR virus typing Eritrea Good (63) WHO Returning traveller reports Fiji Good (69) CDC 2012 outbreak and description of DHF French Polynesia Good (75) CDC 2009 outbreak, PCR virus typing and description of DHF Guinea-Bissau Good (60) WHO, CDC Returning traveller reports, questionnaire response and SE Kiribati Good (71) CDC 2008 outbreak and PCR virus typing Liberia Poor (29) WHO, CDC Reports of sporadic outbreaks and SE Maldives Good (71) WHO 2011 outbreak and seroprevalence survey Marshall Islands Complete (80) CDC 2011 outbreak Mauritius Good (65) WHO 2009 outbreak, seroprevalence survey and PCR virus typing Mayotte Good (75) WHO 2005 outbreak, seroprevalence survey and PCR virus typing Micronesia Good (69) WHO, CDC 2011 outbreak, returning traveller reports, PCR virus typing and description of DHF Netherlands Antilles Good (75) WHO 2008 outbreak and seroprevalence survey Nauru Poor (20) CDC PCR virus typing and SE Niue Good (65) CDC On-going-low level indigenous transmission with reports of sporadic outbreaks and PCR virus typing Northern Mariana Islands Moderate (54) CDC 2001 outbreak and seroprevalence survey Reunion Moderate (43) WHO, CDC 2010 outbreak, PCR virus typing and SE Samoa Good (68) CDC 2001 outbreak, Returning traveller reports, PCR virus typing Seychelles Good (63) WHO 2004 outbreak South Sudan Good (67) WHO PCR virus typing Togo Poor (30) CDC Returning traveller reports and SE Tokelau Good (60) CDC 2001 outbreak Tonga Good (71) CDC 2007 outbreak and returning traveller reports Turks and Caicos Islands Indeterminate (10) WHO Low level background case data, reported cases in peer-reviewed articles and SE Tuvalu Poor (30) CDC 1998 outbreak, description of DHF and SE Wallis and Futuna Good (67) CDC 1998 outbreak, PCR virus typing and SE Table 1 shows countries for which we identified a consensus better than indeterminate on dengue-presence, but was listed as dengue-absent by the WHO or the CDC. WHO = World Health Organization, CDC = Centers for Disease Control, SE = supplementary evidence, PCR = polymerase chain reaction, DHF = dengue haemorrhagic fever. The majority of these newly identified dengue-present countries were in Africa and the evidence type that allowed greatest identification was returning traveller reports. These sporadic reports established preliminary evidence, which we improved with supplementary evidence and questionnaire retrieval to clarify dengue status if possible (Table 2). Outside of Africa, the remaining newly identified countries were almost exclusively islands in the Indian and Pacific Oceans and in the Caribbean. The reason for a lack of dengue presence identification by health organisations here is likely the longer interval between epidemics in small isolated nations, resulting in sparse data which different health organisations have interpreted inconsistently. Inclusion of less official surveillance evidence, such as ProMED reports, that detected background case loads alongside officially reported outbreaks allowed our distinction of these areas as in fact dengue-present. 10.1371/journal.pntd.0001760.t002 Table 2 Evidence consensus class changes in Africa as a result of including supplementary evidence and questionnaire responses. Country Evidence consensus class excluding questionnaires and supplementary evidence Evidence consensus class including questionnaires and supplementary evidence Equatorial Guinea Poor (absence) Indeterminate Mauritania Poor (absence) Indeterminate Niger Poor (absence) Indeterminate Central African Republic Indeterminate Poor Liberia Indeterminate Poor Malawi Indeterminate Poor Uganda Indeterminate Poor Zimbabwe Indeterminate Poor Angola Poor Moderate Benin Poor Moderate Chad Poor Moderate Guinea-Bissau Poor Good Cameroon Moderate Good Côte d'Ivoire Good Complete Nigeria Good Complete Sierra Leone Good Complete All classes refer to consensus on dengue presence unless otherwise stated. Supplementary evidence was available for all countries in this table, while questionnaire responses were received from Cameroon, Burkina Faso, Malawi, Guinea-Bissau, Gabon and Côte d'Ivoire. A total of 3.97 billion people live in these 128 countries outside the indeterminate consensus class. Of these, 824 million live in urban and 763 million in peri-urban areas. These numbers therefore constitute plausible preliminary estimates for the maximum possible population at any risk of dengue transmission. We expect more comprehensive population at risk calculations to refine this figure and quantify levels of risk in our future work, allowing us to give a more accurate estimate. Public display of dengue data varied by continent (Figure 8). In total, 46 of 128 dengue-present countries displayed annual dengue case numbers. Of these, the highest reporting coverage was observed in Asia and the Americas where 55% and 57% of countries respectively reported dengue publically. This figure was comparably worse in the Pacific (29%) and Africa, Saudi Arabia, Yemen and the western Indian Ocean islands (Africa+) where just 7% of dengue-present countries publicly report dengue and none on mainland continental Africa. There were no regional patterns in the level of dengue case data provided, although the publicising of epidemiological weeks in some Central and South American countries tended to provide higher levels of detail. Deaths due to DHF/DSS/severe dengue were far less commonly reported, although the data are available for some Central American countries. Even allowing for variable internet usage and endogenous public health systems, we highlight the magnitude of disparity in countries' provision of freely available dengue data. 10.1371/journal.pntd.0001760.g008 Figure 8 The worldwide variation in governments that publicly display dengue data. The map shows governments that at a minimum display dengue case data at a national level yearly via their official Ministry of Health website. The Americas Dengue presence is well documented in the Americas with a continuous set of good- or complete- consensus countries from southern Brazil to the Mexico-U.S.A. border (Figure 3). However, a general regional classification was not producible as in some cases such as Montserrat and Saint Vincent and the Grenadines, where moderate rather than good consensus was found. With only 22% of dengue-present Caribbean countries displaying dengue data publically, dengue status in these small island nations that are characterised by longer inter-epidemic periods proved considerably more heterogeneous. This was mainly due to a lack of confirmed indigenous cases during recent epidemics. Other regions of uncertainty reflect dynamic dengue status at the limits of the disease distribution. Lower consensus estimates in areas of Florida and Argentina result from reliance on smaller amounts of evidence from recent epidemics. Although the disease extent is better described in Florida (both in terms of resolution and consensus) due to greater data availability, uncertainty is still present due to the unknown persistence of recent events. A similar pattern of uncertainty exists in Texas but for different reasons, being that the occurrence evidence is older and six of seven counties have no record of occurrence since the late 1980s. Africa+ A total of 58% of Africa+ countries had a good consensus or better but Africa still showed the highest levels of uncertainty in countries with poor consensus. Concentrations of higher consensus were identified in East and West Africa (Figure 4). Multiple seroprevalence surveys over several years [31], [32], [33], [34], [35] made the most significant contribution in defining East Africa's higher-consensus cluster which ranges from Sudan to Tanzania with only Uganda, Rwanda and Burundi exhibiting poor or worse evidence consensus. In addition to this, evidence of outbreaks in coastal areas of Yemen, Saudi Arabia and some evidence of spill-over into Egypt added certainty to the definition of the East Africa high-consensus cluster. Although not as contiguous a tract of countries, a higher-consensus region also exists in West Africa from Senegal to Gabon. Inclusion of reported dengue cases in travellers and soldiers returning from West Africa was available for 13 countries and proved the most useful information in this region. Outside of these higher-consensus regions, evidence consensus is low and a series of countries with moderate or worse consensus can be identified from Chad to Mozambique with only the Democratic Republic of Congo exhibiting good evidence consensus. For many of these countries, there are sporadic reports of dengue occurrence combined with poor disease surveillance and a general lack of data. Dated seroprevalence surveys in areas where many other arboviruses are circulating did little to increase certainty. These factors result in a positive evidence consensus that is nevertheless highly uncertain in large portions of Africa. Even where evidence was available from contemporary epidemics, such as in the case of the western Indian Ocean islands, it was often devalued because there was a lack of clinical differentiation between dengue and chikungunya despite epidemics coinciding. The lack of clear clinical distinction between the two diseases [36] makes the scale of dengue here difficult to identify and as a result, some countries (such as Reunion) were identified as having low consensus. Despite the widespread uncertainty in dengue status in many African countries, we were able to differentiate multiple levels of uncertainty. Angola and Mozambique both show lower consensus due to dated evidence forms, yet they are still distinguishable from countries with no evidence or just sporadic occurrences such as Zambia or Congo. Asia A wide variety of contemporary evidence allowed us to display a near continuous distribution of good or complete evidence consensus countries from Indonesia to as far north as Pakistan and Zhejiang, China (Figure 5). Within this dengue-present area, 58% of countries publicly displayed dengue data (Figure 8) and many reported dengue case data with a high spatial resolution. Minor exceptions to this continuous distribution occur in southern China and North-East India largely due to a lack of contemporary evidence. In Gunagxi and Hainan there is little research interest or case data in recent years despite occurrences in urban centres further along the Chinese coast [37], [38], [39]. In North-East India, lower consensus was observed due to a lack of reported cases in recent years combined with the arrival of chikungunya in the area which complicates any potential dengue reporting [40]. Evidence consensus in Asia is lowest in central Asia where contemporary dengue occurrence records combined with low surveillance capacity results in an unclear boundary to the disease. While evidence for dengue presence in the lowland urban centres of Pakistan is accurate and contemporary, reports from the more remote north-west provinces are contemporary, but not accurate [41], [42], [43]. This makes determining the extent further north into remote and data-deficient areas of Afghanistan and central Asia difficult to assess. We also found serologic evidence consistent with dengue presence in Turkey [44] and Kuwait [45], reducing evidence consensus for absence in these countries despite not belonging to any known cluster of dengue-present countries. Europe Although no countries in Europe were defined as dengue-present, sporadic indigenous transmission events have lowered consensus in some countries (Figure 6). Since the invasion and spread of Ae. albopictus along the Mediterranean coast [46], indigenous dengue transmission has been detected in Marseilles, France and Korčula, Croatia (both regions have moderate consensus on dengue absence) and chikungunya has been found in Italy (having good consensus on dengue absence) [47], [48], [49]. These isolated events do not in themselves confer dengue presence, but increased surveillance will be required in light of the Ae. albopictus invasion to maintain this status. This, combined with the lower levels of healthcare expenditure, has led to an observed greater uncertainty in some eastern European states. Australia and Pacific Islands In general, consensus on dengue presence and absence was well defined across Australia and the Pacific islands, with 85% of countries showing good or complete evidence consensus (Figure 7). Where low consensus was observed, it was largely due to a lack of contemporary evidence despite Pacific-wide dengue epidemics such as in Niue, Nauru, Tuvalu and Papua New Guinea. The duration between epidemics is typically longer in the Pacific and consensus is subject to continual change; for example, in the Marshall islands evidence consensus was upgraded from moderate to complete in the wake of the December 2011 epidemic, which came two decades after the last reported epidemic [50]. Such fluctuation is not entirely unexpected from remote, isolated communities, however. Even though evidence consensus decreases with time, it still remains positive, allowing for potential re-occurrence. Lower evidence consensus was observed for Papua New Guinea due to a lack of reported case data since the 1980's, yet multiple literature sources suggest that dengue is still widespread [51], [52], [53]. While dengue occurrence is closely documented in some counties on the Australian coast, the serologic results from Charters Towers has contributed to uncertainty over the inland extent of the disease in Queensland [54]. Only the governments of Australia, New Caledonia and the Solomon Islands report dengue case numbers publicly. Considering the long intervals between epidemics in the Pacific, it is perhaps unsurprising that this is not a priority. Discussion Here we present the distribution of dengue virus transmission as assessed by evidence-based consensus. By emphasising the need for accurate, contemporary evidence through a weighted scoring system, we were able to identify areas where dengue status was more uncertain, particularly in Africa and Central Asia, and identify evidence gaps where surveillance might be better targeted to more accurately assess dengue status. By including a wide variety of evidence we were able to cast doubt on dengue status in countries previously described by health organisations as dengue-absent. While many studies have focussed on the future threat of dengue as a result of range expansion or climate change, this is the first to assess the entirety of knowledge regarding the extent of current virus transmission. We have found that evidence of dengue virus transmission is temporally dynamic and that a contemporary map must emphasise evidence by weighting it appropriately. By increasing temporal resolution to one inter-epidemic period, we have extended the approach of Van Kleef et al. [5] who used evidence from literature searches to produce distribution maps pre- and post- 1975. Focussing on a higher resolution timescale for dengue evidence is necessary if we are to infer changes in the evidence-based distribution of dengue. The suggestion that dengue is an under-recognised problem in Africa is not a new one [55], [56], [57], but here we present a detailed summary of the specific gaps in evidence that exist in different regions. We show that consensus mapping is flexible to regional differences in evidence availability and as such can produce meaningful outputs in resource-high and low settings. The evidence that dengue is widespread in Africa implies that the continent is underrepresented by occurrence points in the model-based approaches that have been used to investigate the distribution of dengue so far [1], [10], [11]. If we are to estimate the burden of dengue in Africa with any fidelity, available data and their underlying assumptions need to be reassessed. Evidence consensus maps provide a more informative alternative to existing country-level maps, such as those provided by the WHO [12] and CDC [58]. As presence or absence exists on a continuous scale of certainty, evidence consensus approaches are more adaptable to incorporating diverse forms of dengue evidence ignored by these organisations in producing their estimates. While we show that different evidence weightings in our scoring system do not significantly alter the result, we were unable to formalise a statistical validation of these weightings due to lack of a training dataset. Our results provide the best estimate thus far of where such data are most needed and comparisons with higher-consensus countries in similar settings should form the first step in directing regional surveillance. Development of methodologies to make approaches such as consensus mapping more reliable is needed as dengue status will increasingly rely on harder-to-quantify evidence types, such as internet search engine terms [59] and multi-language internet text-mining systems [60], [61]. The success of automated disease surveillance systems such as HealthMap [16], [17], and Biocaster [60], [63] have already been demonstrated. We believe evidence consensus provides the best platform for integrating these diverse forms of information now available for disease occurrence to create an up-to-date, high-resolution map of dengue evidence, whilst retaining important assessments of certainty. We also intend to extend our own data collection and accessibility with a new website linked to the Global Health Network (http://globalhealthtrials.tghn.org/) that will allow evidence contribution from members and will provide a key platform for display of dengue data and consensus maps. Although the current approach was used to map the distribution of dengue, minor modifications to the scoring system would allow it to be utilised for a variety of diseases for which the quality of presence evidence is spatially variable. In this work, our aim was to produce a standardised methodology that used the largest variety of evidence to assess country dengue status, whilst still being applicable in diverse healthcare settings and suitable at multiple spatial scales. We considered the stark contrast in evidence available in Africa as compared to the rest of the world. Our results show that the inclusion of supplementary evidence (used in 44% of African countries but only 11% of the rest), healthcare expenditure information (for case data absences) and questionnaires increased evidence consensus in these countries without impacting the methodology applied to the rest of the world. Similarly, we are aware that increasing resolution to Admin1 or Admin2 level may well reduce the evidence available for calculating evidence consensus in each area compared to country-level calculations. As a result, we carefully chose which countries should have increased spatial resolution based on whether sufficient evidence was available in smaller administrative units. We also limited the selection of these countries to those at the limits of the disease's distribution, as data deficiencies in these regions more accurately represent the uncertainty on dengue status given the dynamic nature of global dengue spread. Here we present the most flexible methodology available, to date, for overcoming these problems. We have demonstrated that a systemic approach with relevant optional categories has allowed us to utilise the maximum variety of evidence available for assessing dengue status in the widest variety of situations. We also openly provide a full list of evidence for each country by category (Table S1). We intend to continue data acquisition by including more endogenous, local evidence through questionnaires and local language search methods, which we expect will allow us to further customise our methodology and assess dengue status in places where we are currently uncertain. Mapping by evidence consensus is a useful approach to quantifying contemporary disease evidence and can be further integrated with geo-spatial modelling to produce worldwide continuous surfaces of dengue risk [64]. Current mapping approaches use presence/absence expert opinion maps to sample pseudo-presence or pseudo-absence points to increase the number of data points on which to base their prediction [65], [66], [67], [68]. Pseudo-sampling could be improved by using the continuous scale of evidence consensus to either affect sample number or point weight within the geo-spatial model. This will lead to more robust, higher resolution dengue maps which are currently in progress [69]. By combining uncertainty assessment from consensus mapping with high-resolution predictions using geo-spatial modelling, we will be able to make more accurate predictions of disease burden with associated confidence intervals made explicit. This will then provide a series of up-to-date assessments of global dengue distribution, thus providing key information to assess dengue spread and the impact of control measures. Supporting Information Figure S1 Geographic locations of occurrence data globally. Country colouring is based on evidence based consensus (see main manuscript) with green representing a complete consensus on dengue absence and red a complete consensus on dengue presence. (TIF) Click here for additional data file. Figure S2 Geographic locations of occurrence data in Africa+. Country colouring is based on evidence based consensus (see main manuscript) with green representing a complete consensus on dengue absence and red a complete consensus on dengue presence. (TIF) Click here for additional data file. Figure S3 Geographic locations of occurrence data in Asia. Country colouring is based on evidence based consensus (see main manuscript) with green representing a complete consensus on dengue absence and red a complete consensus on dengue presence. (TIF) Click here for additional data file. Figure S4 Geographic locations of occurrence data in the Americas. Country colouring is based on evidence based consensus (see main manuscript) with green representing a complete consensus on dengue absence and red a complete consensus on dengue presence. (TIF) Click here for additional data file. Figure S5 Geographic locations of occurrence data in Australia. Country colouring is based on evidence based consensus (see main manuscript) with green representing a complete consensus on dengue absence and red a complete consensus on dengue presence. (TIF) Click here for additional data file. Figure S6 Number of occurrence samples per year globally (a) and for Africa+ (b), Asia, (c) the Americas and Australia (d). (TIF) Click here for additional data file. Figure S7 Map of evidence types used for each national and subnational area. Figure S7 shows the different evidence categories used in assessing evidence consensus for each country and Admin1/2 area. HO = health organisation status, L = literary evidence, CD = case data, SE = supplementary evidence, PO = professional opinion. (TIF) Click here for additional data file. Protocol S1 An outline of the dengue occurrence point database construction and content. Data sources, searches and exclusion criteria are outlined and the method of geo-positioning explained. The regional bias of available occurrence points is also given in the accompanying figures. Table S1 shows the collection of evidence used to assess evidence consensus for each country and Admin1 and Admin2 areas. Details of the scoring system can be found in the Methods section of the main manuscript. Scores for each category are highlighted in red. Evidence consensus is calculated as the percentage of the maximum possible score (see Fig. 2 in the main manuscript). HE = healthcare expenditure, DENV = dengue virus, DHF = dengue haemorrhagic fever, DSS = dengue shock syndrome, PCR = polymerase chain reaction, DF = dengue fever. (DOC) Click here for additional data file. Table S1 The collection of evidence used to assess evidence consensus for each country and Admin1 and Admin2 areas. Details of the scoring system can be found in the Methods section of the main manuscript. Scores for each category are highlighted in red. Evidence consensus is calculated as the percentage of the maximum possible score (see Fig. 2 in the main manuscript). HE = healthcare expenditure, DENV = dengue virus, DHF = dengue haemorrhagic fever, PCR = polymerase chain reaction, DF = dengue fever. (DOC) Click here for additional data file.
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Global spread of dengue virus types: mapping the 70 year history
Jane Messina, Oliver J Brady, Thomas W. Scott … (2014)
Early spread and typing of DENV DENV are members of the Flavivirus genus (see Glossary), related to other medically important arboviruses such as yellow fever and Japanese encephalitis viruses. There are four phylogenetically and antigenically distinct dengue viruses (DENV1–4), and although infection with one type confers long-term immunity, it is to that type only and not to the other three [1]. The ancestor of these viruses has been postulated to have emerged about 1 000 years ago in an infectious cycle involving non-human primates and mosquitoes, with transmission to humans having occurred independently for all four virus types only a few hundred years ago [2,3]. Although outbreaks of disease clinically consistent with dengue have been reported for centuries, it was not until 1943 in Japan and 1945 in Hawaii that the first two dengue viruses were isolated (named DENV1 and DENV2, respectively) [4]. At this point, epidemics of dengue illness were being reported across the region spanning from India to the Pacific Islands. In the latter half of the 20th century, DENV transmission followed the spread of its principal mosquito vector, Aedes aegypti [5], and was likely accelerated by urbanization and globalization [6,7]. The collapse of the Ae. aegypti eradication campaign in the Americas in the 1970s was also important in marking the beginning of transport of Asian dengue viruses to the Americas, followed by the rapid re-introduction of the principal mosquito vector throughout both continents [8]. A need for type-specific global maps Spatial patterns in concurrent and/or sequential circulation of DENV1–4 should be considered along with virus and host genetics as potentially important population-level risk factors for severe dengue illness [9,10] because secondary infection with a heterologous DENV type may increase the probability of severe disease [11–14]. Despite this, no study has systematically reviewed all documented spatially explicit evidence of the global spread of the four DENV types since the first isolation in 1943. Rather, the majority of existing studies have focused on the evolution of individual DENV types at regional or local scales [15–20]. Global descriptions of type-specific DENV distribution are few in number, lacking spatial and temporal precision, and are presented in a non-systematic manner [8]. Reported cases do not comprise the entire range of each DENV type at any given time, meaning that a lack of reporting for a specific type at any time or place does not indicate its certain absence. This is due to spatial variability in several factors, namely in the degree of sampling, proportion of infections having been typed, reliability of typing methods, and finally reporting of these types. That said, the use of more advanced typing methods has expanded significantly across the globe since their development, and a thorough description of confirmed presences of each DENV type is needed if we are to gain a better understanding of the global dispersal of the four viruses and track changes moving forward. Here we provide this baseline depiction, also highlighting those geographic areas lacking in information about the specific DENV type(s) responsible for dengue occurrence. Brady et al. [21] recently outlined the definitive extents of dengue presence globally, and their work was followed by that of Bhatt et al. [22] which generated high spatial-resolution (5 km × 5 km) estimates of contemporary global dengue risk and burden in 2010. Our efforts here provide further insight into the global distribution of dengue by reviewing the individual DENV types responsible for reported occurrence throughout the past seven decades. Our aim is to complement phylogenetic and disease occurrence analyses by presenting the sub-national distribution of reported confirmed instances of human infection with each DENV type globally from 1943 to 2013. We provide a more spatially and temporally detailed picture of the spread of each individual DENV type than was previously available, also presenting contemporary maps of the number of DENV types ever reported in an area to elucidate global patterns in their co-circulation and establishment of hyperendemicity. Compiling a global database of DENV type reporting We compiled an extensive database by extracting DENV type and locational information from published literature and case reports spanning the period 1943–2013. In brief, searches were conducted in PubMed (http://www.ncbi.nlm/pubmed) using the terms ‘dengue’ and ‘serotype’ or ‘type,’ and all pseudonyms were automatically included using the Medical Subject Headings (MeSH) terminology. No language restrictions were placed on these searches; however, only those citations with a full title and abstract were retrieved. In-house language skills allowed processing of all English, French, Portuguese, and Spanish articles. We were unable to extract information from a small number of Turkish, Polish, Hebrew, Italian, German, and Chinese articles. ProMED reports (http://www.promedmail.org) were also searched using the terms ‘dengue’ and ‘serotype’ or ‘type’, and all DENV type data that could be linked to a location were extracted, resulting in a search of 1 912 unique articles or reports. DENV type data from national Ministry of Health websites were included where available, resulting in 294 additional sources of information. Finally, DENV envelope protein gene sequences were extracted from GenBank (http://www.ncbi.nlm.nih.gov/genbank), providing another 1 070 sources for a total of 3 276 sources from which type and geographical coordinate information was ultimately extracted. References for these sources are available upon request. Geo-positioning was performed to the finest level of detail possible (e.g., country, province, district, or city/town). In the case of a returning traveler report, we recorded the location visited as the site of the occurrence. The database was last updated on 4 October 2013. The sources we used to create our initial database often described the occurrence of more than one DENV type at a time and/or spanned multiple years. Conversely, it was often the case that multiple sources were referring to the same outbreak. To account for these concerns we derived a standard definition of an occurrence as the following: one or more reports of confirmed infection(s) from a specific DENV type in a given unique location within a single year. Accordingly, if multiple reports confirmed the presence of a DENV type in the same location within the same calendar year, they were considered as a single occurrence record. It is thus important to stress that our definition of a type-specific occurrence does not relate to the actual number of cases reported (this information was not consistently available), but rather to the presence of that DENV type in a given area and year. For mapping and descriptive purposes, province- or state-level administrative units (Admin1) served as our unique locations. When information was only available at the country (Admin0) level in a particular year, it was included in the database for those countries smaller in area than Queensland, Australia (the largest Admin1 unit in our database at 1.7 million km2). From this final Admin1-level occurrence database, a series of global maps were created for each DENV type across six time-periods between 1943 and 2013. We also present the number of DENV types ever reported in a given area across these time-periods. Graphs displaying yearly occurrences by world region and country were also created (Figures S1–S12 in the supplementary material online). Although reports of suspected cases were excluded from the database, no restrictions were made regarding the specific diagnostic typing method used to confirm the DENV type(s) responsible for an occurrence because not all sources specified this information. This was particularly true for ProMED reports that often simply reported cases as confirmed without specifying the method of confirmation; however, for comprehensiveness in our database, the following identification method(s) were recorded: (i) virus isolation, (ii) PCR, (iii) plaque-reduction neutralization test (PRNT), (iv) not specified/other. The frequency of occurrences confirmed by each of these methods is described in Table S1 in the supplementary material online. In some cases, immunoglobulin M (IgM), immunoglobulin G (IgG), or hemagglutination inhibition (HI) assays were used to confirm infection with DENV, but used alone these methods are not useful for type-specific DENV determination owing to DENV type cross-reactivity [23]. As such, these were included in the not specified/other category. Mapping DENV spread A total of 1 956 DENV1 occurrences, 1 931 DENV2 occurrences, 1 631 DENV3 occurrences, and 1 000 DENV4 occurrences were mapped at the Admin1 and small Admin0 level across the entire study period. An additional 1 811 confirmed DENV occurrences were not attributable to a specific type due to lack of testing and/or reporting. Our maps describe the reporting history of each DENV type for the periods 1943–1959, 1960–1969, 1970–1979, 1980–1989, 1990–1999, and 2000–2013 as presented in Figures 1–4. These figures are complemented by additional graphs (Figures S1–S12 in the supplementary material online) that further break down the yearly distribution of occurrences for each DENV type by region (Africa, the Americas, and Asia) and by country within those regions. To simplify the number of categories presented in the supplementary material online, the Americas were considered to include North, Central, and South America, whereas Africa includes the African continent as well as Yemen and Saudi Arabia. The Asia region additionally includes Oceania and the Pacific Islands. The supplementary material also contains the summary of available information on type-specific diagnostic methods (Table S1 in the supplementary material online). Again, this is provided for descriptive purposes only because any occurrence described as confirmed was included in our database, regardless of diagnostic method, to obtain the most comprehensive picture of DENV type distributions. DENV1 Figure 1 displays the global spatial distribution of confirmed DENV1 occurrences by time period. DENV1 was first reported in 1943 in French Polynesia and Japan, followed by reports in Hawaii in 1944 and 1945. It was not until the late 1950s, however, that reporting of DENV1 in the Asian region constantly increased over time. It was first reported in Africa in 1984 in Sudan, and has been sporadically reported in the region ever since, with more continuous periods of reporting in Saudi Arabia between the mid-1990s and mid-2000s, as well as several years of reporting in Reunion in the mid-to-late 2000s. DENV1 was not reported in the Americas until 1977, when it was recorded in Barbados, Cuba, French Antilles, Grenada, Paraguay, and Puerto Rico. After these first recorded occurrences, reporting increased persistently across the region over the next few decades, with near-continuous reporting in Brazil, Mexico, and Puerto Rico in particular. Several other countries in the region began having more sustained reports in the 1980s and 1990s, including Colombia, Costa Rica, French Guiana, Paraguay, Peru, and Venezuela. Reporting of DENV1 peaked in 2005–2006, primarily attributable to recorded occurrences in the Americas. Since 1983, the Pan American Health Organization (PAHO), in collaboration with the US Centers for Disease Control (CDC) Dengue Branch in Puerto Rico, provided technical assistance for developing laboratory surveillance networks in several countries in the region, and this may partially explain increased reporting of all DENV types since this time. DENV2 DENV2 was first reported in 1944 in Papua New Guinea and Indonesia, followed by the Philippines in 1954 and 1956. Malaysia and Thailand have reported many consecutive years of DENV2 occurrence since the early 1960s, as well as Indonesia since the early 1970s and China, India, the Philippines, Sri Lanka, and Singapore since the 1980s. Continuous reporting did not occur in Cambodia and Vietnam until the 1990s. In Africa, DENV2 was reported in Nigeria multiple times between 1964 and 1968, but has not since been reported there. However, several sporadic occurrences have since been reported in the African region, with the most recent reports being from Gabon in 2010 and Kenya in 2013. DENV2 was reported in the Americas as early as 1953 in Trinidad and Tobago, but continuous reporting in the region did not begin until the late 1960s and early 1970s, most notably in Puerto Rico. Since this time, more and more Latin American countries have begun frequent reporting of DENV2, with continuous reporting in Brazil in particular since 1984 accounting for the majority of reporting of this type globally. In the 1990s, there was an increase in the number of the more severe hemorrhagic fever (DHF) cases in the Americas, possibly due to the replacement of the American DENV2 genotype with an imported and more virulent Asian one [24–27]. This increase in DHF cases may be responsible for the noticeable rise in DENV2 reporting in this region since that time, as seen in Figure 2. The largest number of occurrences reported to date was in 2005, with over 100 Admin1 and small Admin0 areas worldwide reporting DENV2 presence, primarily in the Americas. DENV3 DENV3 was first reported in 1953 in the Philippines and Thailand, and has been reported in Asia every year since 1962. Although many countries in Asia have reported DENV3 throughout the study period, Thailand most notably reported DENV3 every year between 1973 and 2010, with the most widespread reporting occurring between 1999 and 2002. Malaysia and Indonesia have also reported DENV3 frequently since the 1970s, as well as Sri Lanka since the early 1980s. Records of DENV3 in China, Vietnam, Cambodia, and Singapore have been fairly consistent since the mid-1990s. The first reports in the Americas were in Puerto Rico in 1963, which continued to report DENV3 until 1978, and then again from 1994 to 2008 owing to the introduction of a new DENV3 genotype from Asia [28]. The majority of other countries in the Americas did not start reporting the type until between the late 1980s and early 2000s. Particularly widespread reporting occurred in Brazil in the mid-2000s. In Africa, overall very little DENV3 has been reported since the first reports in 1984–1985 in Mozambique, and occurrence has mostly been sporadic, with the exception of more frequent reporting between 1994 and 2009 in Saudi Arabia. DENV4 DENV4 was reported first in 1953 in the Philippines and Thailand. Since this time the region has reported DENV4 yearly, most frequently in Thailand whose most widespread reporting occurred between 1999 and 2002. Sri Lanka has also reported DENV4 almost yearly since 1978. Although reporting by country has not been as consistent as for other DENV types, periods of more frequent reporting have occurred in the Indochina region as well as Indonesia, India, Myanmar, and French Polynesia. DENV4 was not reported in the Americas until 1981, when it was reported in Brazil, Cuba, Dominica, Puerto Rico, and the US Virgin Islands. Since this date, reporting has occurred yearly in the region, with particularly frequent reporting in Puerto Rico since the 1980s–1990s, Venezuela and Colombia since the 1990s, and Nicaragua, Brazil, and Peru since the late 1990s–mid 2000s. Co-circulation of DENV types Figure 5 displays the cumulative number of DENV types having been reported in any given Admin1 and small Admin0 area by decade, and highlights the fact that, until the 1980s, the majority of areas had only reported one or two types of DENV. This figure allows the observation of potential increases in co-circulation of the four viruses, which may serve as a key indicator of progression toward hyperendemic transmission [29]. In the late 1980s, the number of types having been reported within a single area began to increase as more cost-effective and less labor-intensive type-specific diagnostic tests (e.g., PCR) were developed for dengue [30]. This may also explain increases in global reporting of specific DENV types at this time, although the number of reported DENV types has since continued to increase in many areas across Latin America and the Caribbean islands, as well as in Southeast Asia, the Indian subcontinent, Indonesia, and Australia. This is particularly noticeable in the map representing 2000–2013, by which time the majority of Brazilian provinces as well as much of Mexico, India, and Indonesia had reported every DENV type. Although few areas in Africa have reported all four DENV types, by the 2000s several areas had reported three. However, it is unclear whether these all represent the persistently transmitted epidemic form of the virus, or are only sporadic overspills from the sylvatic transmission cycle. Exponential growth in DENV type reporting Reporting of DENV type is irregular and affected by many types of bias, in particular in locations with less virological diagnostic capacity, and thus our database represents an opportunistic sample of occurrence. As such, it is important to remember that an absence of reporting for one DENV type is not synonymous with an absence of its occurrence. It is also important to remember that our definition of a type occurrence does not relate to the actual number of cases reported, but rather to the presence of the DENV type in a given area and year. Furthermore, because our definition of a type occurrence does not relate to the actual number of cases reported, but rather the presence of the DENV type in a given area and year, it is impossible to make inferences about the relative prevalence of each type in any given location. That said, the records that support this review comprise the most comprehensive type-specific DENV database to date, and its breadth allows a much more detailed depiction of spread than was previously available. Although there is general agreement that several factors have led to an expansion of the geographic range of dengue over our study period, and particularly in the latter half of the 20th century [6,31–36], the variability in reporting practices over time as well as by region and country must be considered alongside apparent patterns of expansion [37]. Improvements in reporting capacity over time will have had a significant effect on the number of cases being reported, compounded by the fact that increasing co-circulation of DENV types may be associated with more severe disease outcomes which are therefore more likely to be reported. However, this study makes it clear that dengue detection has increased dramatically across the globe since 1943, with some DENV types being newly reported in particular areas more rapidly than others. DENV1 was reported to occur the most times during this 70 year study period, followed by DENV2, DENV3, and DENV4. The overall number of confirmed type-specific DENV events particularly escalated in the 1990s, primarily comprising increases in DENV1 and DENV2 detection in the Americas owing to the availability of rapid diagnostic tests. In Asia, although DENV2 reporting has increased rapidly, DENV3 reporting has surpassed that of DENV1. The greatest increase in reporting of DENV3 occurred in the 1990s, predominantly in the Americas. DENV4 reporting spread the least rapidly during the 70 year period, although it has been consistently reported in ever-greater numbers since the 1980s, particularly in Asia and the Americas. Although documenting dramatic increases in DENV reporting across Asia and the Americas, our review also underscores the fact that there still remains a dearth of type-specific DENV information in many parts of Africa – where our understanding of the evolutionary history and current dynamic of DENV transmission and spread is also poorest. Both research efforts and numbers of reports have remained relatively low here compared to Asia and the Americas, although several arboviral studies have been conducted in the region since the 1980s [38–56]. This problem is made worse by the lack of DENV type-specific information in the few countries where occurrence reports are available. This is particularly true in the countries of east Africa where evidence for dengue presence is high [21], but little information exists about the specific location(s) within these countries where DENV has occurred or about the DENV type(s) responsible for occurrence. Research outlook Additional research questions can be explored with use of the database presented here, such as those surrounding the importance of travel, migration, and commercial trade in the spread of dengue, the introduction of novel DENV types (and/or genotypes) into locations where DENV is or is not already present [57], and DENV population structure and evolution, as well as casting light on how human immunity mediates DENV transmission at the micro and macro scales (Box 1) [58]. The ability to answer these questions will complement phylogenetic studies, and may even be integrated with phylogeographic studies of DENV evolution and dispersal on a regional and global scale [59–61]. The comprehensive database that has been compiled will be made publicly available (via http://figshare.com [62]) such that it can be directly referred to in order to facilitate DENV type identification in historic samples, thereby providing information about what specific viruses were circulating in an area at a particular time. Tracking the spread of DENV types also has important implications for ongoing research [15] analyzing changes in dengue endemicity in a given area over time. Continuing spread has global implications Although international transmission of disease is not new, it is notable that airline passenger numbers have increased by 9% annually since 1960, enabling infected human hosts to move the viruses long distances more quickly [27,63]. Increased urbanization along with substandard housing, unreliable water supply, and poor sanitation provide an environment for Ae. aegypti proliferation in close proximity to human hosts. In the Americas, this may have been exacerbated by the collapse of the Ae. aegypti eradication program in the 1970s [8]. Increases in the size of resource-poor urban populations in Latin America and the Caribbean in the late 1980s and early 1990s may have supported the establishment and spread of all four DENV types in the Americas [64]. According to the United Nations Population Division, between 2000 and 2030, Africa and Asia together are expected to account for four-fifths of all urban growth in the world (see http://esa.un.org/unpd/wpp/Documentation/pdf/WPP2012_Volume-I_Comprehensive-Tables.pdf). Therefore, it is essential that we closely monitor the ongoing and future spread of DENV types in these regions to understand, detect, and respond better to the global burden of dengue disease. Finally, because our maps have underscored the ability of all four DENV types to expand into new territories, the crucial need for a DENV vaccine which protects against all four known types is made ever more apparent [64]. It is also important to note the recent suggestion of a fifth DENV type [65] which, if found to be as transmissible as the other four DENV types, might follow a similar pattern of geographical spread as seen with these types over the past two decades. If sustained transmission of a fifth type did occur in human populations, this would be an additional consideration for vaccine development efforts [64,66], and an up-to-date map of the spread of DENV types would be essential. Concluding remarks In sum, this review offers the most contemporary understanding of DENV type-specific geographic distributions from 1943 to 2013, providing a starting point and rationale for charting the ongoing global spread of each DENV type. Specifically, the increasing co-circulation of types in most regions of the world – particularly in Latin America and Asia – has important implications for patterns in disease severity and hyperendemicity, as well as for ongoing vaccine efforts. It also highlights a paucity of DENV type-specific geographic information in many locations across the globe that needs to be urgently addressed.
Author and article information
Journal
Journal ID (nlm-ta): Emerg Infect Dis
Journal ID (iso-abbrev): Emerg Infect Dis
Journal ID (publisher-id): EID
Title:
Emerging Infectious Diseases
Publisher:
Centers for Disease Control and Prevention
ISSN
(Print):
1080-6040
ISSN
(Electronic):
1080-6059
Publication date
(Print):
January
2021
Volume: 27
Issue: 1
Pages: 130-139
Affiliations
[1]International Vaccine Institute, Seoul, South Korea (J.K. Lim, M. Carabali, J.-S.
Lee, K.S. Lee, M.Y. Shin, S. Namkung, J.S. Yang);
[5]Action, Gouvernance, Intégration, Renforcement Program Equité, Ouagadougou, Burkina
Faso (A. Barro, P.-A. Somé);
Author notes
Address for correspondence: Jacqueline K. Lim, Research Scientist, Global Dengue and
Aedes-transmitted Diseases Consortium, International Vaccine Institute, SNU Research Park,
Gwankak-ro 1, Gwanak-gu, Seoul 151-191, South Korea; email:
kajlim@
123456gmail.com
Article
Publisher ID: 19-1650DOI: 10.3201/eid2701.191650
PMC ID: 7774580
PubMed ID: 33350906
SO-VID: 62b4ee4a-ce0f-4bee-8d42-5367a4493b36
History
Categories
Subject:
Research
Subject:
Research
Subject:
Estimating the Force of Infection for Dengue Virus Using Repeated Serosurveys, Ouagadougou,
Burkina Faso
ScienceOpen disciplines: Infectious disease & Microbiology
Data availability:
ScienceOpen disciplines: Infectious disease & Microbiology