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      COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population

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      Applied Sciences
      MDPI AG

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          Abstract

          The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.

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          Most cited references31

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          The species Severe acute respiratory syndrome-related coronavirus : classifying 2019-nCoV and naming it SARS-CoV-2

          The present outbreak of a coronavirus-associated acute respiratory disease called coronavirus disease 19 (COVID-19) is the third documented spillover of an animal coronavirus to humans in only two decades that has resulted in a major epidemic. The Coronaviridae Study Group (CSG) of the International Committee on Taxonomy of Viruses, which is responsible for developing the classification of viruses and taxon nomenclature of the family Coronaviridae, has assessed the placement of the human pathogen, tentatively named 2019-nCoV, within the Coronaviridae. Based on phylogeny, taxonomy and established practice, the CSG recognizes this virus as forming a sister clade to the prototype human and bat severe acute respiratory syndrome coronaviruses (SARS-CoVs) of the species Severe acute respiratory syndrome-related coronavirus, and designates it as SARS-CoV-2. In order to facilitate communication, the CSG proposes to use the following naming convention for individual isolates: SARS-CoV-2/host/location/isolate/date. While the full spectrum of clinical manifestations associated with SARS-CoV-2 infections in humans remains to be determined, the independent zoonotic transmission of SARS-CoV and SARS-CoV-2 highlights the need for studying viruses at the species level to complement research focused on individual pathogenic viruses of immediate significance. This will improve our understanding of virus–host interactions in an ever-changing environment and enhance our preparedness for future outbreaks.
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            Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study

            Summary Background Since Dec 31, 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV). Cases have been exported to other Chinese cities, as well as internationally, threatening to trigger a global outbreak. Here, we provide an estimate of the size of the epidemic in Wuhan on the basis of the number of cases exported from Wuhan to cities outside mainland China and forecast the extent of the domestic and global public health risks of epidemics, accounting for social and non-pharmaceutical prevention interventions. Methods We used data from Dec 31, 2019, to Jan 28, 2020, on the number of cases exported from Wuhan internationally (known days of symptom onset from Dec 25, 2019, to Jan 19, 2020) to infer the number of infections in Wuhan from Dec 1, 2019, to Jan 25, 2020. Cases exported domestically were then estimated. We forecasted the national and global spread of 2019-nCoV, accounting for the effect of the metropolitan-wide quarantine of Wuhan and surrounding cities, which began Jan 23–24, 2020. We used data on monthly flight bookings from the Official Aviation Guide and data on human mobility across more than 300 prefecture-level cities in mainland China from the Tencent database. Data on confirmed cases were obtained from the reports published by the Chinese Center for Disease Control and Prevention. Serial interval estimates were based on previous studies of severe acute respiratory syndrome coronavirus (SARS-CoV). A susceptible-exposed-infectious-recovered metapopulation model was used to simulate the epidemics across all major cities in China. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credibile interval (CrI). Findings In our baseline scenario, we estimated that the basic reproductive number for 2019-nCoV was 2·68 (95% CrI 2·47–2·86) and that 75 815 individuals (95% CrI 37 304–130 330) have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6·4 days (95% CrI 5·8–7·1). We estimated that in the baseline scenario, Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461 (95% CrI 227–805), 113 (57–193), 98 (49–168), 111 (56–191), and 80 (40–139) infections from Wuhan, respectively. If the transmissibility of 2019-nCoV were similar everywhere domestically and over time, we inferred that epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan outbreak of about 1–2 weeks. Interpretation Given that 2019-nCoV is no longer contained within Wuhan, other major Chinese cities are probably sustaining localised outbreaks. Large cities overseas with close transport links to China could also become outbreak epicentres, unless substantial public health interventions at both the population and personal levels are implemented immediately. Independent self-sustaining outbreaks in major cities globally could become inevitable because of substantial exportation of presymptomatic cases and in the absence of large-scale public health interventions. Preparedness plans and mitigation interventions should be readied for quick deployment globally. Funding Health and Medical Research Fund (Hong Kong, China).
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              Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle

              Since December 2019, a total of 41 cases of pneumonia of unknown etiology have been confirmed in Wuhan city, Hubei Province, China. Wuhan city is a major transportation hub with a population of more than 11 million people. Most of the patients visited a local fish and wild animal market last month. At a national press conference held today, Dr Jianguo Xu, an academician of the Chinese Academy of Engineering, who led a scientific team announced that a new‐type coronavirus, tentatively named by World Health Organization as the 2019‐new coronavirus (2019‐nCoV), had caused this outbreak. 1 The 2019‐nCoV has a different coronavirus‐specific nucleic acid sequence from known human coronavirus species, which are similar to some of the beta coronaviruses identified in bats. 2 , 3 The virus‐specific nucleic acid sequences were detected in lung fluid, blood and throat swab samples in 15 patients and the virus that was isolated showed a typical coronavirus appearance under electron microscopy. Further research will be conducted to better understand the new coronavirus to develop antiviral agents and vaccines. 4 We applauded the excellent job that has been done so far. The infection was first described in December. Within 9 days, a special team consisted of physicians, scientists and epidemiologists who ruled out several extremely contagious pathogens including SARS, which killed hundreds of people more than a decade ago, and MERS. This has surely alleviated environmental concerns as Hong Kong authorities had quickly stepped up the disinfection of trains and airplanes and checks of passengers due to this outbreak. Most of the patients visited the fish and wild animal market last month in Wuhan. This fish and wild animal market also sold live animals such as poultry, bats, marmots, and snakes. All patients received prompt supportive treatment in quarantine. Among them, seven patients were in serious condition and one patient died. All of the 42 patients so far confirmed were from China except one Thailand patient who was a traveler from Wuhan. Eight patients have been cured of the disease and were discharged from the hospital last week. The 2019‐nCoV now have been isolated from multiple patients and appears to be the culprit. But the mystery has not been completely solved yet. Until there is a formal published scientific manuscript, the facts can be argued, particularly regarding causality despite these facts having been officially announced. The data collected so far is not enough to confirm the causal relationship between the new‐type coronavirus and the respiratory disease based on classical Koch's postulates or modified ones as suggested by Fredricks and Relman. 5 The viral‐specific nucleic acids were only discovered in 15 patients, and successful virus culture was extremely limited to only a few patients. There remains considerable work to be done to differentiate between colonization, shedding, and infection. Additional strains of the 2019‐nCoV need to be isolated to study their homologies. It is expected that antigens and monoclonal antibodies will be developed so serology can be used to confirm previous and acute infection status. The episode demonstrates further the need for rapid and accurate detection and identification methods that can be used in the local hospitals and clinics bearing the burden of identifying and treating patients. Recently, the Clinical Laboratory Improvement Amendments (CLIA) of 1988 has waived highly sensitive and specific molecular devices known as CLIA‐waived devices so that these devices are gradually becoming available for point of care testing. Finally, the epidemiological similarity between this outbreak and that of SARS in 2002‐2003 6 is striking. SARS was then traced to animal markets 7 and eventually to palm civets. 8 Later bats were identified as animal reservoirs. 9 Could this novel coronavirus be originated from wild animals? The family Coronaviridae includes two subfamilies. 10 One, the subfamily Coronavirinae, contains a substantial number of pathogens of mammals that individually cause a remarkable variety of diseases, including pneumonia. In humans, coronaviruses are among the spectrum of viruses that cause the common cold as well as more severe respiratory disease—specifically SARS and MERS, which are both zoonoses. The second subfamily, Torovirinae, contains pathogens of both terrestrial and aquatic animals. The genus Torovirus includes the type species, equine torovirus (Berne virus), which was first isolated from a horse with diarrhea, and the Breda virus, which was first isolated from neonatal calves with diarrhea. White bream virus from fish is the type species of the genus Bafinivirus. However, there is no evidence so far that the seafood from the fish and animal market caused 2019‐nCoV‐associated pneumonia. This epidemiologic similarity clearly provides a starting point for the further investigation of this outbreak. In the meantime, this fish and animal market has been closed until the epidemiological work determines the animal host of this novel coronavirus. Only then will the miracle be complete.
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                Author and article information

                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                June 2020
                June 03 2020
                : 10
                : 11
                : 3880
                Article
                10.3390/app10113880
                7636875d-b366-4c36-b507-e9bb5e5185a6
                © 2020

                https://creativecommons.org/licenses/by/4.0/

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