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      Performance of case definitions and clinical predictors for influenza surveillance among patients followed in a rural cohort in Senegal

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          Abstract

          Background

          Influenza is a major cause of morbidity and mortality in Africa. However, a lack of epidemiological data remains for this pathology, and the performances of the influenza-like illness (ILI) case definitions used for sentinel surveillance have never been evaluated in Senegal. This study aimed to i) assess the performance of three different ILI case definitions, adopted by the WHO, USA-CDC (CDC) and European-CDC (ECDC) and ii) identify clinical factors associated with a positive diagnosis for Influenza in order to develop an algorithm fitted for the Senegalese context.

          Methods

          All 657 patients with a febrile pathological episode (FPE) between January 2013 and December 2016 were followed in a cohort study in two rural villages in Senegal, accounting for 1653 FPE observations with nasopharyngeal sampling and influenza virus screening by rRT-PCR. For each FPE, general characteristics and clinical signs presented by patients were collected. Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) for the three ILI case definitions were assessed using PCR result as the reference test. Associations between clinical signs and influenza infection were analyzed using logistic regression with generalized estimating equations. Sore throat, arthralgia or myalgia were missing for children under 5 years.

          Results

          WHO, CDC and ECDC case definitions had similar sensitivity (81.0%; 95%CI: 77.0–85.0) and NPV (91.0%; 95%CI: 89.0–93.1) while the WHO and CDC ILI case definitions had the highest specificity (52.0%; 95%CI: 49.1–54.5) and PPV (32.0%; 95%CI: 30.0–35.0). These performances varied by age groups. In children < 5 years, the significant predictors of influenza virus infection were cough and nasal discharge. In patients from 5 years, cough, nasal discharge, sore throat and asthenia grade 3 best predicted influenza infection. The addition of “nasal discharge” as a symptom to the WHO case definition decreased sensitivity but increased specificity, particularly in the pediatric population.

          Conclusion

          In summary, all three definitions studies (WHO, ECDC & CDC) have similar performance, even by age group. The revised WHO ILI definition could be chosen for surveillance purposes for its simplicity. Symptomatic predictors of influenza virus infection vary according the age group.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12879-020-05724-x.

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          Most cited references28

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          Statistical analysis of correlated data using generalized estimating equations: an orientation.

          J Hanley (2003)
          The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end-users appreciate the essence of the method. The examples are simple enough to see the behind-the-scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data.
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            Risk Factors for Severe Outcomes following 2009 Influenza A (H1N1) Infection: A Global Pooled Analysis

            Introduction In late April 2009, a novel strain of influenza A H1N1 was identified in Mexico and the United States. This virus quickly spread globally, and on June 11, 2009, the World Health Organization (WHO) declared a pandemic alert phase 6, indicating that the first influenza pandemic of the 21st century had begun [1]–[3]. Many Northern hemisphere temperate countries experienced their first wave of infection during the spring and summer months of 2009, followed by an early 2009 fall influenza season. Southern hemisphere temperate countries experienced the first wave of infection during their winter of 2009, and at the time of writing are finishing their winter 2010 season. By the end of 2009, the peak of the local influenza epidemic had passed in most countries around the world [4]. Since the start of the pandemic, WHO and member states have been gathering information to characterize the clinical picture and patterns of risk associated with the 2009 pandemic influenza A H1N1 (H1N1pdm) virus infection to assist public health policy makers in targeting of vaccination strategies, antiviral use, and other control measures. Risk factors for severe disease following seasonal influenza infection have been well documented in many countries, and include chronic medical conditions such as pulmonary, cardiovascular, renal, hepatic, neuromuscular, hematologic, and metabolic disorders, some cognitive conditions, and immunodeficiency [5]–[7]. The risk associated with seasonal influenza during pregnancy is less well documented but in previous pandemics, pregnant women were identified as being at increased risk of adverse outcomes, and many countries include healthy pregnant women among the seasonal influenza high risk groups as well [8]–[11]. However, early in the 2009 H1N1 pandemic, risk factors for severe disease following infection were largely unknown. Following a series of teleconferences organized by WHO with clinicians treating H1N1pdm patients around the world, it appeared that the most common risk factors for severe H1N1pdm disease were similar to those for seasonal influenza infection; however, several new factors (e.g., obesity and tuberculosis [TB]) were also observed with high frequency in some countries. It was also noted that members of indigenous/aboriginal communities in some countries appeared to be overrepresented among severe cases [12]. While many countries have recently reported data on the association between severe H1N1pdm influenza and the presence of a variety of underlying risk factors (e.g., [13]–[26]), these data are presented in different formats, making direct comparisons across countries difficult, and no clear consensus has emerged for some conditions. This paper presents data from approximately 70,000 lab-confirmed hospitalized and 2,500 fatal cases of H1N1pdm infection in 19 countries or administrative regions—Argentina, Australia, Canada, Chile, China, France, Germany, Hong Kong SAR, Japan, Madagascar, Mexico, the Netherlands, New Zealand, Singapore, South Africa, Spain, Thailand, the United States, and the United Kingdom—in order to characterize and compare the distribution of underlying risk factors among H1N1pdm confirmed patients who were hospitalized, admitted to an intensive care unit (ICU), or died, and to assess the frequency and distribution of known and new potential risk factors for severe H1N1pdm infection. Methods This study compares data primarily obtained from surveillance programs of the Ministries of Health or National Public Health Institutes of 19 countries or administrative regions covering the period 1 April 2009 to 1 January 2010. Countries were asked to provide risk factor data on laboratory-confirmed cases using a standardized format for this analysis. The data were collected in the course of routine surveillance, methods of which varied from country to country [27]–[49], and were reported anonymously and as aggregate data; hence, no ethics approval was required. It should be noted that considerable effort was put into negotiating permission for these data to be presented in these formats. As many countries would not be willing to have their country-specific data published in direct comparisons with others, we are taking the approach of publishing data from a wide range of countries and showing the variability observed, so that results from specific studies can be compared with the international results reported here. Potential risk factors were grouped into four categories: age, chronic medical illnesses, pregnancy (by trimester), and “other,” which included conditions that were not previously considered as risk factors for severe influenza outcomes, such as obesity, membership in a vulnerable social or ethnic group, and TB. Details of the standardized format and definitions of each of the conditions are provided in Text S1. Risk factor information was collected separately for three levels of severity of illness in laboratory-confirmed patients: hospitalizations, admissions to ICU, and fatalities by country. Details of the available data from countries by risk factor and severity level are provided in . For each risk factor, except for pregnancy, the percentage of patients who were hospitalized, were admitted to ICU, and died was calculated using the total number of cases reported in each severity category. To evaluate the risk associated with pregnancy, the ratio of pregnant women to all women of childbearing age (age 15–49 y) in each level of severity was used to describe the differences between levels. The overall median and interquartile ranges (IQRs) were calculated for each risk factor using all available data. In addition, where available, countries provided baseline comparison data for prevalence of the risk factor in the general population (details and sources provided in Text S1). Data on age were provided by age groups ( 40 5 3.0 (1.4–11.5) 5 5.0 (3.4–16.4) 6 15.2 (4.0–30.8) 2 15.0 (9.5–20.4) 2 36.3 (22.4–50.1) BMI not measured but judged clinically obese 8 4.3 (1.8–13.3) 4 4.4 (3.4–5.3) 8 7.8 (3.8–17.3) NA NA Vulnerable social/ethnic group 4 5.2 (2.3–10.6) 4 5.0 (1.5–10.7) 4 10.1 (5.3–18.5) 4 1.0 (0.2–3.7) 4 2.4 (1.2–3.8) TB 2 1.7 (0.9–1.8) 2 1.3 (1.0–1.6) 4 2.6 (0.8–5.9) NA NA a See Text S1 for definitions of risk factors. b All data given as median percent (IQR), except for age, which is median (in years) (IQR). c RRhosp is the unadjusted RR of hospitalization among H1N1pdm patients with the risk factor compared to the risk of hospitalization among H1N1pdm patients without the risk factor, and RRdeath is the unadjusted RR of death among H1N1pdm patients with the risk factor compared to the risk of death among H1N1pdm patients without the risk factor; range of RR provided if ≥2 countries provided data. d The number of countries providing data for cell directly to the right; the full list of countries that provided data for each risk factor is provided in Text S1. e RRhosp and RRdeath calculated by age group and shown in Figure 1. f Denominator is women of childbearing age in each level of severity. NA, not assessed. Chronic Illness The proportion of H1N1pdm patients with at least one chronic medical condition generally increased with severity (median among all countries that provided data was 31.1% [n = 14], 52.3% [n = 10], and 61.8% [n = 16] of hospitalized, ICU-admitted, and fatal H1N1pdm cases, respectively (Table 1). This pattern was observed for most countries (individual country data not shown). For nearly every individual risk factor under study, the prevalence increased significantly with severity level. Chronic respiratory conditions excluding asthma (median = 10.3%, 17.2%, and 20.4%, respectively) and asthma (median = 17.6%, 9.8%, and 5.3%, respectively) were the risk factors most often reported among severe cases, followed closely by diabetes (median = 9.0%, 13.6%, and 14.4%, respectively) and chronic cardiac conditions (median = 7.1%, 10.9%, and 12.1%, respectively). The pooled OR for death given hospitalization was significantly above one for each risk factor listed, with the exception of asthma, and was highest for chronic liver disease and immunocompromised patients (Figure 3). 10.1371/journal.pmed.1001053.g003 Figure 3 Pooled odds ratio and 95% CIs of risk of death given hospitalization for selected countries. See Text S1 for countries included in the pooled risk factor ORs. The risk of severe disease due to H1N1 infection, including hospitalization and death, was elevated for every chronic condition for which data were available (Table 1). Notably, the RR for fatal disease due to H1N1pdm infection was elevated for asthma (median RRdeath = 1.7 [IQR 1.5–2.1]) and not markedly different from the RR associated with hospitalizations (median RRhosp = 1.8 [IQR 1.2–2.6]). Data on chronic illness rates in the general population were not available from enough countries to permit an assessment of the relative magnitude of risk associated with various conditions with certainty. Pregnancy The proportions of women of childbearing age who were hospitalized with H1N1 and were pregnant as part of all hospitalizations (median of all country data = 17.4% [IQR 13.5–30.2]), who were admitted to ICU (median of all country data = 15.0% [IQR 9.4–24.2]), and who died (median of all country data = 6.9% [0.0–9.1]) varied within each country. Pregnant women in their third trimester consistently accounted for more than half of all pregnant women among hospitalized, ICU-admitted, and fatal cases. However, with the exception of China, Thailand, and the US, the proportion of pregnant women decreased with increasing level of severity, and the pooled OR for death given hospitalization during pregnancy was below 1 (pooled OR = 0.6, 95% CI 0.2–2.5). Pregnant women with H1N1pdm infection were at higher risk of hospitalization than women of childbearing age in the general population without H1N1pdm infection, with an unadjusted RR of hospitalization ranging from 3.5 in Germany to 25.3 in France (median RRhosp = 6.8, n = 10 countries). The unadjusted RR of death, while elevated compared to non-pregnant women in more than half of countries, was generally lower than that for hospitalization, with a median RRdeath of 1.9 (n = 11 countries). Four areas (Japan, the Netherlands, Hong Kong SAR, and Singapore) had a RRdeath of zero. Other Risk Factors The proportion of patients with obesity (body mass index [BMI] ≥30 or clinically judged as obese) increased with increasing disease severity and represented a median of 6%, 11.3%, and 12.0% of all hospitalized, ICU-admitted, and fatal H1N1pdm cases, respectively, and this pattern was also observed for morbid obesity (BMI >40), with 3.0%, 5.0%, and 15.2%, respectively (Table 1). However, this pattern was not consistently reported in each country. For example, France, Thailand, and China observed similar proportions of obese patients among ICU-admitted and fatal cases, while Hong Kong SAR reported a lower prevalence of obesity among fatal cases than among ICU admissions. Using data from all countries, the pooled OR for death given hospitalization for obesity (BMI ≥30 or clinically judged as obese) was 2.9 (95% CI 1.3–6.6; Figure 3). Compared to the general population in the two countries for which data were available, the risk of death associated with morbid obesity was increased (mean RRdeath = 36.3 [IQR 22.4–50.1], n = 2). Canada, Australia, and New Zealand reported significant disparities in the burden of severe H1N1pdm disease across different ethnic groups. In these three countries, indigenous population groups were overrepresented among severe H1N1pdm cases requiring hospitalization and among fatal cases. In contrast, in Thailand and Mexico, minority groups were underrepresented among severe H1N1pdm cases. Taken together, the unadjusted median RR of hospitalization for H1N1pdm patients among minority groups was 1.0 (IQR 0.2–3.7) and the median RR of death was 2.4 (IQR 1.2–3.8). TB data were reported from three countries, and the incidence increased slightly with level of severity. The disease was reported in a median of 1.7%, 1.3%, and 2.6% of hospitalized, ICU-admitted, and fatal H1N1pdm cases, respectively. We were not specifically able to evaluate HIV incidence because of a paucity of data on HIV in H1N1pdm patients. Discussion Our analysis represents to our knowledge the first comprehensive assessment of the frequency and distribution of risk factors for severe H1N1pdm infection from a global perspective, with data from approximately 70,000 patients requiring hospitalization, 9,700 patients admitted to ICU, and 2,500 fatalities from 19 countries and administrative regions around the world. Consistent with other published data, our results reaffirm that the age distribution of severe H1N1pdm cases significantly differs from that of seasonal influenza [53]–[56]. The highest rates of hospitalization per capita were in children <15 y, but the highest rates of mortality per capita were in persons over 64 y. The low apparent attack rate in the oldest age group, evidenced by low rates of hospitalization, and the high odds associated with age in the fatal group compared to hospitalized cases seems to indicate that although older adults may have a lower risk of infection, they have a significantly higher risk of death if they are infected [54],[57]–[61]. It is likely that increasing prevalence of chronic risk conditions in the oldest age group contributes to this effect, but our data do not allow for quantification of this association. Our results demonstrate that in a significant portion of severe and fatal cases, patients had preexisting chronic illness, and that the presence of chronic illness increased the likelihood of death. It was notable, however, that approximately 2/3 of hospitalized cases and 40% of fatal cases did not have any identified preexisting chronic illness. It is unknown how many of these cases had other risk factors, such as pregnancy, obesity, and substance abuse (including smoking and alcohol), for which we had insufficient information in this study. These figures are also dependent on the completeness of available data for recorded risk factors. As with seasonal influenza, the most common underlying chronic conditions among hospitalized patients were respiratory disease, asthma, cardiac disease, and diabetes. Interestingly, we found that although asthma was frequently associated with both hospitalization and death in most countries, with an increased RR for both, the OR for death given hospitalization suggested that a higher proportion of hospitalized cases survived compared to patients with other conditions. This may represent the occurrence of manageable influenza-induced exacerbations of asthma prompting admission that do not progress to viral pneumonia or other fatal complications, and may also reflect the fact that asthma tends to occur in younger age groups [62]. Early data suggested that pregnancy might be an important risk factor for severe disease with H1N1pdm [21],[25],[63],[64]. Our analysis is consistent with these reports and more recent studies [47],[65], which found an overall trend that pregnant women, mainly in their third trimester, have a higher incidence of hospitalization than the general population. Several published studies have also shown that pregnancy is associated with a higher risk of ICU admission and fatal outcome [54],[58],[66],[67]. In our analysis, the risk associated with pregnancy was elevated for both hospitalization and fatality compared to women of childbearing age, though the latter association was not consistently observed in every country. As with asthma, the proportion of pregnant women generally decreased with severity level for most of the countries. Our results suggest that pregnant women with H1N1pdm are approximately seven times more likely to be hospitalized and two times more likely to die than non-pregnant women with H1N1pdm. The greater risk for hospitalization than for death with H1N1pdm influenza infection during pregnancy may have resulted from a lower threshold for admitting infected pregnant women to hospital and/or a more aggressive approach to antiviral or other treatment for pregnant women. In addition, the occurrence of non-respiratory complications of pregnancy, such as hypertension, pre-eclampsia, and premature labor, provoked by H1N1pdm infection may have increased the risk of hospitalization while not resulting in death [68]. This would be consistent with published reports of case series of pregnant patients that list complications of pregnancy as a common cause of admission [63],[69],[70]. The dataset did not allow us to adjust for underlying conditions in pregnant women, and thus to distinguish between risks for healthy pregnant women, and pregnant women with underlying medical conditions; however, we believe that the results support an approach of early intervention with pregnant women who develop influenza. Early in the 2009 pandemic, clinicians from the US reported a surprisingly high prevalence of morbid obesity, a risk factor not previously associated with severe outcomes for seasonal influenza infection, in patients with severe complications of H1N1pdm infection [71]. Subsequent studies in several countries, including the US, Mexico, Canada, Spain, Greece, France, Australia, and New Zealand, reported high proportions of obesity among ICU admissions and fatal cases [13],[20],[58],[64],[72]–[77]. Our results provide supportive evidence that obesity may be a risk factor for severe disease, as seen in the increasing proportion of morbidly obese patients with severity level and the associated elevated OR. Our findings also suggest that morbidly obese patients with H1N1pdm are more likely to die if hospitalized; however, the results in our analysis were not consistent across all countries. The association between obesity (or morbid obesity) and severe outcomes may reflect direct causation (e.g., due to greater respiratory strain of infection on obese individuals), causation through other known risk factors (e.g., obesity causes diabetes and heart disease, which pose an increased risk for severe outcome [36]), or a noncausal association, if some other factor (e.g., genetic or dietary) caused both morbid obesity and increased risk of severe outcome. Unfortunately, our dataset did not allow us to distinguish among these nonexclusive alternatives. Indigenous populations and ethnic minorities have been reported to experience a disproportionately high burden of severe H1N1pdm infection, particularly in the Americas [14],[21],[23],[36],[64],[75],[78]–[80] and the Australasia-Pacific region [43],[80]–[82], similar to reports during the 1918 influenza pandemic [83]–[85]. Our analysis of Australian, New Zealand, and Canadian data concur with these published reports, and while compelling, were not universal. Neither Thailand nor Mexico observed a significantly increased burden of severe H1N1pdm disease among indigenous or minority populations. Our data are not sufficient to explain the observed differences in the reported risk of severe disease among minority groups, but several hypotheses have been proposed, including a higher prevalence of chronic medical conditions known to increase risk of severe influenza, delayed or reduced access to healthcare, cultural differences in healthcare-seeking behavior and approaches to health, potential differences in genetic susceptibility, and social inequalities [23],[78],[80]. More research is needed to better understand and quantify the increased risk of severe H1N1pdm disease among these groups. However, an imperfect understanding of the mechanisms of health disparities related to severe H1N1pdm disease should not impede the public health community in undertaking actions to mitigate this risk by disseminating appropriate public information, targeting outreach and prevention programs, and involving at-risk population groups in pandemic planning. Our analysis has a number of limitations, not least of which is the wide differences in surveillance systems, case management policies, and antiviral use in the countries studied. The criteria and indications for hospital and ICU admission for certain conditions (e.g., pregnancy and asthma) and by age (e.g., pediatric patients) varied significantly by country, and may have been somewhat dependent on capacity for admission, which likely varied over time. Risk factors are also dependent on the completeness and quality of data on risk factors reported and classification of death in the absence of complete testing. These variables could lead to a bias in the estimate of these conditions among severe cases and could make direct comparisons across countries difficult. Second, our data do not consider multiple risk factors for individual H1N1pdm patients. A lack of individual-level data on underlying medical conditions of H1N1pdm patients precludes our ability to sufficiently control for confounding and therefore identify the independent contribution of individual risk factors for severe disease and death. The differences observed in risk factors for hospitalization and death among H1N1pdm patients compared to among seasonal influenza patients, and the wide range of RR values between countries may be explained by differences in age structure in the general population. Several studies have identified important differences in the proportions of underlying conditions by age among hospitalized and fatal cases, including, but not limited to, the UK [15],[53], the US [36], Canada [47], and Singapore [39],[86]. A third limitation is related to our imperfect calculation of the point prevalence of pregnancy among women of childbearing age in the general population. However, we believe that our findings of the range of RR values for hospitalization and death is valid, but may be very slightly inflated because of undercounting in the denominator. The inflationary effect of undercounting is likely greatest for pregnant women in the first trimester, as we didn't adjust for common first trimester events such as miscarriages or abortions, and in this group there is likely substantial undercounting in the numerator as well because of women not knowing they are pregnant in that period. Fourth, the data used in our analysis relied on hospital records, which were not standardized, and were likely to be incomplete or vary in quality between hospitals or countries. This poses a problem in the direct comparativeness between settings. Despite these limitations, this analysis is the first to our knowledge to compare risk factors across a variety of countries using data from a very large number of patients, and we found a great deal of consistency for much of the data. Clearly, cardiac disease, chronic respiratory disease, and diabetes are important risk factors for severe disease that will be especially relevant for countries with high rates of these illnesses. We provide evidence to support the concern regarding obesity, particularly morbid obesity, as a risk factor, though this needs more study. We found large between-country variations for some important risk factors, most notably pregnancy, and the reasons for these differences need more study. There is evidence to suggest that the differences observed for pregnancy might represent differences in case management practices, and we believe that the available evidence supports vaccination and early intervention for pregnant women. Our study reinforces the need to identify and target high-risk groups for interventions, such as immunization, information, early medical advice, and use of antiviral medications. Experience with the 2009 H1N1 pandemic and the differences observed between countries have highlighted the need for country-specific surveillance data and global standardization of case definitions and data collection, and the usefulness of data sharing to aid policy makers in critical decision making for global influenza epidemics. Supporting Information Text S1 Supplemental data and analysis. (PDF) Click here for additional data file.
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              The rise and fall of malaria in a West African rural community, Dielmo, Senegal, from 1990 to 2012: a 22 year longitudinal study.

              A better understanding of the effect of malaria control interventions on vector and parasite populations, acquired immunity, and burden of the disease is needed to guide strategies to eliminate malaria from highly endemic areas. We monitored and analysed the changes in malaria epidemiology in a village community in Senegal, west Africa, over 22 years. Between 1990 and 2012, we did a prospective longitudinal study of the inhabitants of Dielmo, Senegal, to identify all episodes of fever and investigate the relation between malaria host, vector, and parasite. Our study included daily medical surveillance with systematic parasite detection in individuals with fever. We measured parasite prevalence four times a year with cross-sectional surveys. We monitored malaria transmission monthly with night collection of mosquitoes. Malaria treatment changed over the years, from quinine (1990-94), to chloroquine (1995-2003), amodiaquine plus sulfadoxine-pyrimethamine (2003-06), and finally artesunate plus amodiaquine (2006-12). Insecticide-treated nets (ITNs) were introduced in 2008. We monitored 776 villagers aged 0-101 years for 2 378 150 person-days of follow-up. Entomological inoculation rate ranged from 142·5 infected bites per person per year in 1990 to 482·6 in 2000, and 7·6 in 2012. Parasite prevalence in children declined from 87% in 1990 to 0·3 % in 2012. In adults, it declined from 58% to 0·3%. We recorded 23 546 fever episodes during the study, including 8243 clinical attacks caused by Plasmodium falciparum, 290 by Plasmodium malariae, and 219 by Plasmodium ovale. Three deaths were directly attributable to malaria, and two to severe adverse events of antimalarial drugs. The incidence of malaria attacks ranged from 1·50 attacks per person-year in 1990 to 2·63 in 2000, and to only 0·046 in 2012. The greatest changes were associated with the replacement of chloroquine and the introduction of ITNs. Malaria control policies combining prompt treatment of clinical attacks and deployment of ITNs can nearly eliminate parasite carriage and greatly reduce the burden of malaria in populations exposed to intense perennial malaria transmission. The choice of drugs seems crucial. Rapid decline of clinical immunity allows rapid detection and treatment of novel infections and thus has a key role in sustaining effectiveness of combining artemisinin-based combination therapy and ITNs despite increasing pyrethroid resistance. Pasteur Institutes of Dakar and Paris, Institut de Recherche pour le Développement, and French Ministry of Cooperation. Copyright © 2014 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                abarry@pasteur.sn
                Journal
                BMC Infect Dis
                BMC Infect Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                7 January 2021
                7 January 2021
                2021
                : 21
                : 31
                Affiliations
                [1 ]GRID grid.418508.0, ISNI 0000 0001 1956 9596, Institut Pasteur de Dakar, Unité d’Epidémiologie des maladies infectieuses, ; 36, Avenue Pasteur, Dakar, Sénégal
                [2 ]Organisation Mondiale de la Santé-Dakar, Dakar, Sénégal
                [3 ]Ministère de la Santé et de l’Action Sociale, Direction de la Prévention, Dakar, Sénégal
                [4 ]GRID grid.418508.0, ISNI 0000 0001 1956 9596, Institut Pasteur de Dakar, Pôle de Virologie, ; Dakar, Sénégal
                Author information
                http://orcid.org/0000-0002-3956-8782
                Article
                5724
                10.1186/s12879-020-05724-x
                7790019
                33413174
                3d83fade-0b70-4ef4-ae9c-c523e262149f
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 February 2020
                : 18 December 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000016, U.S. Department of Health and Human Services;
                Award ID: IDSEP140020-01-00
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Infectious disease & Microbiology
                performance,influenza,surveillance,senegal
                Infectious disease & Microbiology
                performance, influenza, surveillance, senegal

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