Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.
Identifieur interne : 000907 ( Main/Exploration ); précédent : 000906; suivant : 000908Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.
Auteurs : Ram Kumar Singh [Inde] ; Meenu Rani [Inde] ; Akshaya Srikanth Bhagavathula [Émirats arabes unis] ; Ranjit Sah [Népal] ; Alfonso J. Rodriguez-Morales [États-Unis] ; Himangshu Kalita [Inde] ; Chintan Nanda [Inde] ; Shashi Sharma [États-Unis] ; Yagya Datt Sharma [États-Unis] ; Ali A. Rabaan [Arabie saoudite] ; Jamal Rahmani [Iran] ; Pavan Kumar [Inde]Source :
- JMIR public health and surveillance [ 2369-2960 ] ; 2020.
Descripteurs français
- KwdFr :
- MESH :
- statistiques et données numériques : Santé mondiale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Humains, Modèles statistiques, Pandémies, Prévision.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- statistics & numerical data : Global Health.
- Forecasting, Humans, Models, Statistical, Pandemics.
Abstract
BACKGROUND
The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019.
OBJECTIVE
The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months.
METHODS
The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19.
RESULTS
The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results.
CONCLUSIONS
The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.
DOI: 10.2196/19115
PubMed: 32391801
PubMed Central: PMC7223426
Affiliations:
- Arabie saoudite, Inde, Iran, Népal, Émirats arabes unis, États-Unis
- Caroline du Sud, Maryland, Virginie
Links toward previous steps (curation, corpus...)
Le document en format XML
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Rabaan</name><affiliation wicri:level="1"><nlm:affiliation>Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran, Saudi Arabia.</nlm:affiliation><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran</wicri:regionArea><wicri:noRegion>Dhahran</wicri:noRegion></affiliation></author><author><name sortKey="Rahmani, Jamal" sort="Rahmani, Jamal" uniqKey="Rahmani J" first="Jamal" last="Rahmani">Jamal Rahmani</name><affiliation wicri:level="1"><nlm:affiliation>Department of Community Nutrition, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Community Nutrition, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Kumar, Pavan" sort="Kumar, Pavan" uniqKey="Kumar P" first="Pavan" last="Kumar">Pavan Kumar</name><affiliation wicri:level="1"><nlm:affiliation>College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi</wicri:regionArea><wicri:noRegion>Jhansi</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32391801</idno><idno type="pmid">32391801</idno><idno type="doi">10.2196/19115</idno><idno type="pmc">PMC7223426</idno><idno type="wicri:Area/Main/Corpus">001355</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">001355</idno><idno type="wicri:Area/Main/Curation">001355</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">001355</idno><idno type="wicri:Area/Main/Exploration">001355</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.</title><author><name sortKey="Singh, Ram Kumar" sort="Singh, Ram Kumar" uniqKey="Singh R" first="Ram Kumar" last="Singh">Ram Kumar Singh</name><affiliation wicri:level="1"><nlm:affiliation>Department of Natural Resources, TERI School of Advanced Studies, New Delhi, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Natural Resources, TERI School of Advanced Studies, New Delhi</wicri:regionArea><wicri:noRegion>New Delhi</wicri:noRegion></affiliation></author><author><name sortKey="Rani, Meenu" sort="Rani, Meenu" uniqKey="Rani M" first="Meenu" last="Rani">Meenu Rani</name><affiliation wicri:level="1"><nlm:affiliation>Kumaun University, Nainital, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Kumaun University, Nainital</wicri:regionArea><wicri:noRegion>Nainital</wicri:noRegion></affiliation></author><author><name sortKey="Bhagavathula, Akshaya Srikanth" sort="Bhagavathula, Akshaya Srikanth" uniqKey="Bhagavathula A" first="Akshaya Srikanth" last="Bhagavathula">Akshaya Srikanth Bhagavathula</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.</nlm:affiliation><country xml:lang="fr">Émirats arabes unis</country><wicri:regionArea>Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain</wicri:regionArea><wicri:noRegion>Al Ain</wicri:noRegion></affiliation></author><author><name sortKey="Sah, Ranjit" sort="Sah, Ranjit" uniqKey="Sah R" first="Ranjit" last="Sah">Ranjit Sah</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Medicine, Tribhuvan University, Kathmandu, Nepal.</nlm:affiliation><country xml:lang="fr">Népal</country><wicri:regionArea>Institute of Medicine, Tribhuvan University, Kathmandu</wicri:regionArea><wicri:noRegion>Kathmandu</wicri:noRegion></affiliation></author><author><name sortKey="Rodriguez Morales, Alfonso J" sort="Rodriguez Morales, Alfonso J" uniqKey="Rodriguez Morales A" first="Alfonso J" last="Rodriguez-Morales">Alfonso J. Rodriguez-Morales</name><affiliation wicri:level="2"><nlm:affiliation>Public Health and Infection Research Group, Faculty of Health Sciences, Universidad Tecnologica de Pereira, Colombia, SC, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Public Health and Infection Research Group, Faculty of Health Sciences, Universidad Tecnologica de Pereira, Colombia, SC</wicri:regionArea><placeName><region type="state">Caroline du Sud</region></placeName></affiliation></author><author><name sortKey="Kalita, Himangshu" sort="Kalita, Himangshu" uniqKey="Kalita H" first="Himangshu" last="Kalita">Himangshu Kalita</name><affiliation wicri:level="1"><nlm:affiliation>Department of Science & Technology, Haryana Space Applications Centre, Hisar, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Science & Technology, Haryana Space Applications Centre, Hisar</wicri:regionArea><wicri:noRegion>Hisar</wicri:noRegion></affiliation></author><author><name sortKey="Nanda, Chintan" sort="Nanda, Chintan" uniqKey="Nanda C" first="Chintan" last="Nanda">Chintan Nanda</name><affiliation wicri:level="1"><nlm:affiliation>Department of Science & Technology, Haryana Space Applications Centre, Hisar, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Science & Technology, Haryana Space Applications Centre, Hisar</wicri:regionArea><wicri:noRegion>Hisar</wicri:noRegion></affiliation></author><author><name sortKey="Sharma, Shashi" sort="Sharma, Shashi" uniqKey="Sharma S" first="Shashi" last="Sharma">Shashi Sharma</name><affiliation wicri:level="2"><nlm:affiliation>Anchor Systems Corporation, Reston, VA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Anchor Systems Corporation, Reston, VA</wicri:regionArea><placeName><region type="state">Virginie</region></placeName></affiliation></author><author><name sortKey="Sharma, Yagya Datt" sort="Sharma, Yagya Datt" uniqKey="Sharma Y" first="Yagya Datt" last="Sharma">Yagya Datt Sharma</name><affiliation wicri:level="2"><nlm:affiliation>Hughes Systique Corporation, Germantown, MD, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Hughes Systique Corporation, Germantown, MD</wicri:regionArea><placeName><region type="state">Maryland</region></placeName></affiliation></author><author><name sortKey="Rabaan, Ali A" sort="Rabaan, Ali A" uniqKey="Rabaan A" first="Ali A" last="Rabaan">Ali A. Rabaan</name><affiliation wicri:level="1"><nlm:affiliation>Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran, Saudi Arabia.</nlm:affiliation><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran</wicri:regionArea><wicri:noRegion>Dhahran</wicri:noRegion></affiliation></author><author><name sortKey="Rahmani, Jamal" sort="Rahmani, Jamal" uniqKey="Rahmani J" first="Jamal" last="Rahmani">Jamal Rahmani</name><affiliation wicri:level="1"><nlm:affiliation>Department of Community Nutrition, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Community Nutrition, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Kumar, Pavan" sort="Kumar, Pavan" uniqKey="Kumar P" first="Pavan" last="Kumar">Pavan Kumar</name><affiliation wicri:level="1"><nlm:affiliation>College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi</wicri:regionArea><wicri:noRegion>Jhansi</wicri:noRegion></affiliation></author></analytic><series><title level="j">JMIR public health and surveillance</title><idno type="eISSN">2369-2960</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Coronavirus Infections (epidemiology)</term><term>Forecasting (MeSH)</term><term>Global Health (statistics & numerical data)</term><term>Humans (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Humains (MeSH)</term><term>Infections à coronavirus (épidémiologie)</term><term>Modèles statistiques (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (épidémiologie)</term><term>Prévision (MeSH)</term><term>Santé mondiale (statistiques et données numériques)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Global Health</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Santé mondiale</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Forecasting</term><term>Humans</term><term>Models, Statistical</term><term>Pandemics</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Modèles statistiques</term><term>Pandémies</term><term>Prévision</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019.</p></div><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b></p><p>The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32391801</PMID><DateCompleted><Year>2020</Year><Month>05</Month><Day>26</Day></DateCompleted><DateRevised><Year>2020</Year><Month>05</Month><Day>26</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2369-2960</ISSN><JournalIssue CitedMedium="Internet"><Volume>6</Volume><Issue>2</Issue><PubDate><Year>2020</Year><Month>05</Month><Day>13</Day></PubDate></JournalIssue><Title>JMIR public health and surveillance</Title><ISOAbbreviation>JMIR Public Health Surveill</ISOAbbreviation></Journal><ArticleTitle>Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.</ArticleTitle><Pagination><MedlinePgn>e19115</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.2196/19115</ELocationID><Abstract><AbstractText Label="BACKGROUND">The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019.</AbstractText><AbstractText Label="OBJECTIVE">The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months.</AbstractText><AbstractText Label="METHODS">The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19.</AbstractText><AbstractText Label="RESULTS">The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results.</AbstractText><AbstractText Label="CONCLUSIONS">The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.</AbstractText><CopyrightInformation>©Ram Kumar Singh, Meenu Rani, Akshaya Srikanth Bhagavathula, Ranjit Sah, Alfonso J Rodriguez-Morales, Himangshu Kalita, Chintan Nanda, Shashi Sharma, Yagya Datt Sharma, Ali A Rabaan, Jamal Rahmani, Pavan Kumar. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 13.05.2020.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Singh</LastName><ForeName>Ram Kumar</ForeName><Initials>RK</Initials><Identifier Source="ORCID">0000-0003-0335-7542</Identifier><AffiliationInfo><Affiliation>Department of Natural Resources, TERI School of Advanced Studies, New Delhi, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rani</LastName><ForeName>Meenu</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0002-9716-177X</Identifier><AffiliationInfo><Affiliation>Kumaun University, Nainital, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bhagavathula</LastName><ForeName>Akshaya Srikanth</ForeName><Initials>AS</Initials><Identifier Source="ORCID">0000-0002-0581-7808</Identifier><AffiliationInfo><Affiliation>Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sah</LastName><ForeName>Ranjit</ForeName><Initials>R</Initials><Identifier Source="ORCID">0000-0002-2695-8714</Identifier><AffiliationInfo><Affiliation>Institute of Medicine, Tribhuvan University, Kathmandu, Nepal.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rodriguez-Morales</LastName><ForeName>Alfonso J</ForeName><Initials>AJ</Initials><Identifier Source="ORCID">0000-0001-9773-2192</Identifier><AffiliationInfo><Affiliation>Public Health and Infection Research Group, Faculty of Health Sciences, Universidad Tecnologica de Pereira, Colombia, SC, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kalita</LastName><ForeName>Himangshu</ForeName><Initials>H</Initials><Identifier Source="ORCID">0000-0002-9620-0052</Identifier><AffiliationInfo><Affiliation>Department of Science & Technology, Haryana Space Applications Centre, Hisar, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nanda</LastName><ForeName>Chintan</ForeName><Initials>C</Initials><Identifier Source="ORCID">0000-0001-6502-2145</Identifier><AffiliationInfo><Affiliation>Department of Science & Technology, Haryana Space Applications Centre, Hisar, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sharma</LastName><ForeName>Shashi</ForeName><Initials>S</Initials><Identifier Source="ORCID">0000-0003-4744-8753</Identifier><AffiliationInfo><Affiliation>Anchor Systems Corporation, Reston, VA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sharma</LastName><ForeName>Yagya Datt</ForeName><Initials>YD</Initials><Identifier Source="ORCID">0000-0002-1152-2141</Identifier><AffiliationInfo><Affiliation>Hughes Systique Corporation, Germantown, MD, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rabaan</LastName><ForeName>Ali A</ForeName><Initials>AA</Initials><Identifier Source="ORCID">0000-0002-6774-9847</Identifier><AffiliationInfo><Affiliation>Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran, Saudi Arabia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rahmani</LastName><ForeName>Jamal</ForeName><Initials>J</Initials><Identifier Source="ORCID">0000-0001-7121-7838</Identifier><AffiliationInfo><Affiliation>Department of Community Nutrition, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kumar</LastName><ForeName>Pavan</ForeName><Initials>P</Initials><Identifier Source="ORCID">0000-0003-3653-8163</Identifier><AffiliationInfo><Affiliation>College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>05</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>Canada</Country><MedlineTA>JMIR Public Health Surveill</MedlineTA><NlmUniqueID>101669345</NlmUniqueID><ISSNLinking>2369-2960</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName></SupplMeshList><CitationSubset>H</CitationSubset><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="CommentIn"><RefSource>Fertil Steril. 2020 Mar 25;:null</RefSource><PMID Version="1">32222253</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005544" MajorTopicYN="N">Forecasting</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014943" MajorTopicYN="N">Global Health</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">ARIMA models</Keyword><Keyword MajorTopicYN="Y">COVID-19</Keyword><Keyword MajorTopicYN="Y">SARS-COV2</Keyword><Keyword MajorTopicYN="Y">coronavirus</Keyword><Keyword MajorTopicYN="Y">forecast</Keyword><Keyword MajorTopicYN="Y">prediction</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>04</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>05</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>05</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>5</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>5</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>5</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId 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