1
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Demers J, Fagan WF, Potluri S, Calabrese JM. The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19. Infect Dis Model 2023; 8:514-538. [PMID: 37250860 PMCID: PMC10186984 DOI: 10.1016/j.idm.2023.04.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 05/31/2023] Open
Abstract
The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.
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Affiliation(s)
- Jeffery Demers
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rosendorf (HZDR), Görlitz, Germany
- Dept. of Biology, University of Maryland, College Park, MD, USA
| | - William F Fagan
- Dept. of Biology, University of Maryland, College Park, MD, USA
| | - Sriya Potluri
- Dept. of Biology, University of Maryland, College Park, MD, USA
| | - Justin M Calabrese
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rosendorf (HZDR), Görlitz, Germany
- Dept. of Biology, University of Maryland, College Park, MD, USA
- Dept. of Ecological Modelling, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
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2
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Iyaniwura SA, Rabiu M, David JF, Kong JD. The basic reproduction number of COVID-19 across Africa. PLoS One 2022; 17:e0264455. [PMID: 35213645 PMCID: PMC8880647 DOI: 10.1371/journal.pone.0264455] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/10/2022] [Indexed: 12/15/2022] Open
Abstract
The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31-4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions.
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Affiliation(s)
- Sarafa A. Iyaniwura
- Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, BC, Canada
- * E-mail:
| | - Musa Rabiu
- School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban, South Africa
| | - Jummy F. David
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada
- Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario, Canada
| | - Jude D. Kong
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Ontario, Canada
- Laboratory for Applied and Industrial Mathematics (LIAM), York University, Toronto, Ontario, Canada
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3
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Cao Z, Qiu Z, Tang F, Liang S, Wang Y, Long H, Chen C, Zhang B, Zhang C, Wang Y, Tang K, Tang J, Chen J, Yang C, Xu Y, Yang Y, Xiao S, Tian D, Jiang G, Du X. Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: a systematic analysis based on an interpretable machine learning framework. Transbound Emerg Dis 2022; 69:e1584-e1594. [PMID: 35192224 DOI: 10.1111/tbed.14492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 02/20/2022] [Accepted: 02/21/2022] [Indexed: 11/26/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine related data, were collected from 39 countries up to June 30, 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied with an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zicheng Cao
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Zekai Qiu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Feng Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,Foshan Center for Disease Control and Prevention, Foshan, 528010, P.R. China
| | - Shiwen Liang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,Fujian Provincial Center for Disease Control and Prevention, Fuzhou, 350001, P.R. China
| | - Yinghan Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,Clinical research center, Second Affiliated Hospital of Kunming Medical University, Kunming, 650033, P.R. China
| | - Haoyu Long
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Cai Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China
| | - Bing Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Chi Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Yaqi Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Kang Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Jing Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Junhong Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Chunhui Yang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Yuzhe Xu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Yulin Yang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Shenglan Xiao
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Dechao Tian
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275, P.R. China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, P.R. China.,Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, 510030, P.R. China
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4
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Biswas S, Mandal AK. Optimization strategies of human mobility during the COVID-19 pandemic: A review. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:7965-7978. [PMID: 34814284 DOI: 10.3934/mbe.2021395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The impact of the ongoing COVID-19 pandemic is being felt in all spheres of our lives - cutting across the boundaries of nation, wealth, religions or race. From the time of the first detection of infection among the public, the virus spread though almost all the countries in the world in a short period of time. With humans as the carrier of the virus, the spreading process necessarily depends on the their mobility after being infected. Not only in the primary spreading process, but also in the subsequent spreading of the mutant variants, human mobility plays a central role in the dynamics. Therefore, on one hand travel restrictions of varying degree were imposed and are still being imposed, by various countries both nationally and internationally. On the other hand, these restrictions have severe fall outs in businesses and livelihood in general. Therefore, it is an optimization process, exercised on a global scale, with multiple changing variables. Here we review the techniques and their effects on optimization or proposed optimizations of human mobility in different scales, carried out by data driven, machine learning and model approaches.
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Affiliation(s)
- Soumyajyoti Biswas
- Department of Physics, SRM University, AP-Amaravati 522502, Andhra Pradesh, India
| | - Amit Kr Mandal
- Department of Computer Science and Engineering, SRM University-AP, Andhra Pradesh 522502, India
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5
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Gutierrez B, Márquez S, Prado-Vivar B, Becerra-Wong M, Guadalupe JJ, da Silva Candido D, Fernandez-Cadena JC, Morey-Leon G, Armas-Gonzalez R, Andrade-Molina DM, Bruno A, de Mora D, Olmedo M, Portugal D, Gonzalez M, Orlando A, Drexler JF, Moreira-Soto A, Sander AL, Brünink S, Kühne A, Patiño L, Carrazco-Montalvo A, Mestanza O, Zurita J, Sevillano G, du Plessis L, McCrone JT, Coloma J, Trueba G, Barragán V, Rojas-Silva P, Grunauer M, Kraemer MU, Faria NR, Escalera-Zamudio M, Pybus OG, Cárdenas P. Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.03.31.21254685. [PMID: 33851177 PMCID: PMC8043474 DOI: 10.1101/2021.03.31.21254685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Characterisation of SARS-CoV-2 genetic diversity through space and time can reveal trends in virus importation and domestic circulation, and permit the exploration of questions regarding the early transmission dynamics. Here we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the COVID-19 pandemic. We generate and analyse 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylgeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions (NPIs), with differential degrees of persistence and national dissemination.
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Affiliation(s)
- Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, UK
- Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador
| | - Sully Márquez
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Belén Prado-Vivar
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Mónica Becerra-Wong
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Juan José Guadalupe
- Laboratorio de Biotecnología Vegetal, Universidad San Francisco de Quito, Quito, Ecuador
| | | | - Juan Carlos Fernandez-Cadena
- Omics Sciences Laboratory, Faculty of Medical Sciences, Universidad de Especialidades Espíritu Santo, Samborondón, Ecuador
| | - Gabriel Morey-Leon
- Faculty of Medical Sciences, Universidad de Guayaquil, Guayaquil, Ecuador
| | - Rubén Armas-Gonzalez
- Faculty of Sciences, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador
| | - Derly Madeleiny Andrade-Molina
- Omics Sciences Laboratory, Faculty of Medical Sciences, Universidad de Especialidades Espíritu Santo, Samborondón, Ecuador
| | - Alfredo Bruno
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
- Universidad Agraria del Ecuador
| | - Domenica de Mora
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Maritza Olmedo
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Denisse Portugal
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Manuel Gonzalez
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Alberto Orlando
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Jan Felix Drexler
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Andres Moreira-Soto
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Anna-Lena Sander
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Sebastian Brünink
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Arne Kühne
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Leandro Patiño
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | | | - Orson Mestanza
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Jeannete Zurita
- Facultad de Medicina, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
- Unidad de Investigaciones en Biomedicina, Zurita & Zurita Laboratorios, Quito, Ecuador
| | - Gabriela Sevillano
- Unidad de Investigaciones en Biomedicina, Zurita & Zurita Laboratorios, Quito, Ecuador
| | | | - John T. McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Josefina Coloma
- School of Public Health, University of California, Berkeley, USA
| | - Gabriel Trueba
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Verónica Barragán
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | | | - Michelle Grunauer
- Escuela de Medicina, Universidad San Francisco de Quito, Quito, Ecuador
| | | | - Nuno R. Faria
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, UK
| | | | - Oliver G. Pybus
- Department of Zoology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK
| | - Paúl Cárdenas
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
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6
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Gutierrez B, Márquez S, Prado-Vivar B, Becerra-Wong M, Guadalupe JJ, Candido DDS, Fernandez-Cadena JC, Morey-Leon G, Armas-Gonzalez R, Andrade-Molina DM, Bruno A, De Mora D, Olmedo M, Portugal D, Gonzalez M, Orlando A, Drexler JF, Moreira-Soto A, Sander AL, Brünink S, Kühne A, Patiño L, Carrazco-Montalvo A, Mestanza O, Zurita J, Sevillano G, Du Plessis L, McCrone JT, Coloma J, Trueba G, Barragán V, Rojas-Silva P, Grunauer M, Kraemer MUG, Faria NR, Escalera-Zamudio M, Pybus OG, Cárdenas P. Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador. Virus Evol 2021; 7:veab051. [PMID: 34527281 PMCID: PMC8244811 DOI: 10.1093/ve/veab051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/12/2021] [Accepted: 06/03/2021] [Indexed: 12/23/2022] Open
Abstract
Characterisation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic diversity through space and time can reveal trends in virus importation and domestic circulation and permit the exploration of questions regarding the early transmission dynamics. Here, we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the coronavirus-19 pandemic. We generated and analysed 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylogeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions, with differential degrees of persistence and national dissemination.
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Affiliation(s)
- Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | - Sully Márquez
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Belén Prado-Vivar
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Mónica Becerra-Wong
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Juan José Guadalupe
- Laboratorio de Biotecnología Vegetal, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | | | - Juan Carlos Fernandez-Cadena
- Omics Sciences Laboratory, Faculty of Medical Sciences, Universidad de Especialidades Espíritu Santo, Samborondón 092301, Ecuador
| | - Gabriel Morey-Leon
- Faculty of Medical Sciences, Universidad de Guayaquil, Guayaquil 090613, Ecuador
| | - Rubén Armas-Gonzalez
- Faculty of Sciences, Escuela Superior Politécnica del Litoral, Guayaquil 090112, Ecuador
| | - Derly Madeleiny Andrade-Molina
- Omics Sciences Laboratory, Faculty of Medical Sciences, Universidad de Especialidades Espíritu Santo, Samborondón 092301, Ecuador
| | - Alfredo Bruno
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Domenica De Mora
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Maritza Olmedo
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Denisse Portugal
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Manuel Gonzalez
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Alberto Orlando
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Jan Felix Drexler
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Andres Moreira-Soto
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Anna-Lena Sander
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Sebastian Brünink
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Arne Kühne
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Leandro Patiño
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | | | - Orson Mestanza
- Servicio de Genética, Instituto Nacional de Salud del Niño San Borja, Lima 15037, Perú
| | - Jeannete Zurita
- Facultad de Medicina, Pontificia Universidad Católica del Ecuador, Quito 170143, Ecuador
| | - Gabriela Sevillano
- Unidad de Investigaciones en Biomedicina, Zurita & Zurita Laboratorios, Quito 170104, Ecuador
| | - Louis Du Plessis
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | - John T McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3JW, UK
| | - Josefina Coloma
- School of Public Health, University of California, Berkeley CA 94704, USA
| | - Gabriel Trueba
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Verónica Barragán
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Patricio Rojas-Silva
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Michelle Grunauer
- Escuela de Medicina, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | | | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | - Paúl Cárdenas
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
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