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Oliveira JF, Alencar AL, Cunha MCLS, Vasconcelos AO, Cunha GG, Miranda RB, Filho FMHS, Silva C, Gustani-Buss E, Khouri R, Cerqueira-Silva T, Landau L, Barral-Netto M, Ramos PIP. Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation study. Lancet Digit Health 2024; 6:e570-e579. [PMID: 39059889 DOI: 10.1016/s2589-7500(24)00099-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 05/03/2024] [Accepted: 05/10/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes. METHODS In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017-23). We constructed a graph-based representation of Brazil's mobility network. The Ford-Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25-April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6-March 1, 2021), for the purposes of model validation. FINDINGS We found that flights alone transported 79·9 million (95% CI 58·3-101·4 million) passengers annually within Brazil during 2017-22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25-April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6-16, 2021. INTERPRETATION By providing essential clues for effective pathogen surveillance, our results have the potential to inform public health policy and improve future pandemic response efforts. Our results unlock the potential of designing country-wide clinical sample collection networks with mobility data-informed approaches, an innovative practice that can improve current surveillance systems. FUNDING Rockefeller Foundation.
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Affiliation(s)
- Juliane F Oliveira
- Center for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (Fiocruz), Salvador, Brazil; Centre of Mathematics of the University of Porto (CMUP), Department of Mathematics, University of Porto, Porto, Portugal.
| | - Andrêza L Alencar
- Department of Computer Science, Federal Rural University of Pernambuco, Recife, Brazil
| | - Maria Célia L S Cunha
- Luiz Coimbra Institute of Graduate and Engineering Research (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Adriano O Vasconcelos
- Luiz Coimbra Institute of Graduate and Engineering Research (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Gerson G Cunha
- Luiz Coimbra Institute of Graduate and Engineering Research (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Ray B Miranda
- Luiz Coimbra Institute of Graduate and Engineering Research (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Fábio M H S Filho
- Rondônia Oswaldo Cruz Foundation, Oswaldo Cruz Foundation (Fiocruz), Porto Velho, Brazil
| | - Corbiniano Silva
- Luiz Coimbra Institute of Graduate and Engineering Research (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Emanuele Gustani-Buss
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven-University of Leuven, Leuven, Belgium
| | - Ricardo Khouri
- Medicine and Precision Public Health Laboratory (MeSP2), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (Fiocruz), Salvador, Brazil
| | - Thiago Cerqueira-Silva
- Center for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (Fiocruz), Salvador, Brazil
| | - Luiz Landau
- Luiz Coimbra Institute of Graduate and Engineering Research (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Manoel Barral-Netto
- Center for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (Fiocruz), Salvador, Brazil; Medicine and Precision Public Health Laboratory (MeSP2), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (Fiocruz), Salvador, Brazil
| | - Pablo Ivan P Ramos
- Center for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (Fiocruz), Salvador, Brazil
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2
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Ondrikova N, Clough H, Douglas A, Vivancos R, Itturiza-Gomara M, Cunliffe N, Harris JP. Comparison of statistical approaches to predicting norovirus laboratory reports before and during COVID-19: insights to inform public health surveillance. Sci Rep 2023; 13:21457. [PMID: 38052922 PMCID: PMC10697939 DOI: 10.1038/s41598-023-48069-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
Social distancing interrupted transmission patterns of contact-driven infectious agents such as norovirus during the Covid-19 pandemic. Since routine surveillance of norovirus was additionally disrupted during the pandemic, traditional naïve forecasts that rely only on past public health surveillance data may not reliably represent norovirus activity. This study investigates the use of statistical modelling to predict the number of norovirus laboratory reports in England 4-weeks ahead of time before and during Covid-19 pandemic thus providing insights to inform existing practices in norovirus surveillance in England. We compare the predictive performance from three forecasting approaches that assume different underlying structure of the norovirus data and utilized various external data sources including mobility, air temperature and relative internet searches (Time Series and Regularized Generalized Linear Model, and Quantile Regression Forest). The performance of each approach was evaluated using multiple metrics, including a relative prediction error against the traditional naive forecast of a five-season mean. Our data suggest that all three forecasting approaches improve predictive performance over the naïve forecasts, especially in the 2020/21 season (30-45% relative improvement) when the number of norovirus reports reduced. The improvement ranged from 7 to 22% before the pandemic. However, performance varied: regularized regression incorporating internet searches showed the best forecasting score pre-pandemic and the time series approach achieved the best results post pandemic onset without external data. Overall, our results demonstrate that there is a significant value for public health in considering the adoption of more sophisticated forecasting tools, moving beyond traditional naïve methods, and utilizing available software to enhance the precision and timeliness of norovirus surveillance in England.
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Affiliation(s)
- Nikola Ondrikova
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
- Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK.
- NIHR Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK.
| | - Helen Clough
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- NIHR Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
| | - Amy Douglas
- National Surveillance Gastrointestinal Pathogens Unit, UK Health Security Agency, London, UK
| | - Roberto Vivancos
- NIHR Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
- Health Protection Operations, UK Health Security Agency, Liverpool, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, UK
| | | | - Nigel Cunliffe
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- NIHR Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UK
| | - John P Harris
- Health Protection Operations, UK Health Security Agency, Liverpool, UK
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3
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Lythgoe KA, Golubchik T, Hall M, House T, Cahuantzi R, MacIntyre-Cockett G, Fryer H, Thomson L, Nurtay A, Ghafani M, Buck D, Green A, Trebes A, Piazza P, Lonie LJ, Studley R, Rourke E, Smith D, Bashton M, Nelson A, Crown M, McCann C, Young GR, Andre Nunes dos Santos R, Richards Z, Tariq A, Fraser C, Diamond I, Barrett J, Walker AS, Bonsall D. Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey. Proc Biol Sci 2023; 290:20231284. [PMID: 37848057 PMCID: PMC10581763 DOI: 10.1098/rspb.2023.1284] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/08/2023] [Indexed: 10/19/2023] Open
Abstract
The Office for National Statistics Coronavirus (COVID-19) Infection Survey (ONS-CIS) is the largest surveillance study of SARS-CoV-2 positivity in the community, and collected data on the United Kingdom (UK) epidemic from April 2020 until March 2023 before being paused. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing the sequenced samples collected by the ONS-CIS during this period. We observed a series of sweeps or partial sweeps, with each sweeping lineage having a distinct growth advantage compared to their predecessors, although this was also accompanied by a gradual fall in average viral burdens from June 2021 to March 2023. The sweeps also generated an alternating pattern in which most samples had either S-gene target failure (SGTF) or non-SGTF over time. Evolution was characterized by steadily increasing divergence and diversity within lineages, but with step increases in divergence associated with each sweeping major lineage. This led to a faster overall rate of evolution when measured at the between-lineage level compared to within lineages, and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens.
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Affiliation(s)
- Katrina A. Lythgoe
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Sydney Infectious Diseases Institute (Sydney ID), School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - Roberto Cahuantzi
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Helen Fryer
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Laura Thomson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Anel Nurtay
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Mahan Ghafani
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - David Buck
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Angie Green
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Amy Trebes
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Paolo Piazza
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Lorne J. Lonie
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | | | | | - Darren Smith
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Matthew Bashton
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Andrew Nelson
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Matthew Crown
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Clare McCann
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Gregory R. Young
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Rui Andre Nunes dos Santos
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Zack Richards
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Adnan Tariq
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | | | | | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | | | - Jeff Barrett
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - David Bonsall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
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4
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Tian L, Liang C, Huang X, Liu Z, Su J, Guo C, Zhu G, Sun J. Genomic epidemiology of dengue in Shantou, China, 2019. Front Public Health 2023; 11:1035060. [PMID: 37522010 PMCID: PMC10374217 DOI: 10.3389/fpubh.2023.1035060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 06/23/2023] [Indexed: 08/01/2023] Open
Abstract
Objectives Dengue has been endemic in Southeast Asian countries for decades. There are few reports tracing the dynamics of dengue in real time. In this study, we generated hundreds of pathogen genomes to understand the genomic epidemiology of an outbreak in a hyper-endemic area of dengue. Methods We leveraged whole-genome short-read sequencing (PE150) to generate genomes of the dengue virus and investigated the genomic epidemiology of a dengue virus transmission in a mesoscale outbreak in Shantou, China, in 2019. Results The outbreak was sustained from July to December 2019. The total accumulated number of laboratory-confirmed cases was 944. No gender bias or fatalities were recorded. Cambodia and Singapore were the main sources of imported dengue cases (74.07%, n = 20). A total of 284 dengue virus strains were isolated, including 259 DENV-1, 24 DENV-2, and 1 DENV-3 isolates. We generated the entire genome of 252 DENV isolates (229 DENV-1, 22 DENV-2, and 1 DENV-3), which represented 26.7% of the total cases. Combined epidemiological and phylogenetic analyses indicated multiple independent introductions. The internal transmission evaluations and transmission network reconstruction supported the inference of phylodynamic analysis, with high Bayes factor support in BSSVS analysis. Two expansion founders and transmission chains were detected in CCH and LG of Shantou. Conclusions We observed the instant effects of genomic epidemiology in monitoring the dynamics of DENV and highlighted its prospects for real-time tracing of outbreaks of other novel agents in the future.
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Affiliation(s)
- Lina Tian
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China
| | - Chumin Liang
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xiaorong Huang
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Zhe Liu
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Juan Su
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Chuan Guo
- Center for Disease Control and Prevention of Shantou City, Shantou, Guangdong, China
| | - Guanghu Zhu
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China
| | - Jiufeng Sun
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
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5
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Nadeau SA, Vaughan TG, Beckmann C, Topolsky I, Chen C, Hodcroft E, Schär T, Nissen I, Santacroce N, Burcklen E, Ferreira P, Jablonski KP, Posada-Céspedes S, Capece V, Seidel S, Santamaria de Souza N, Martinez-Gomez JM, Cheng P, Bosshard PP, Levesque MP, Kufner V, Schmutz S, Zaheri M, Huber M, Trkola A, Cordey S, Laubscher F, Gonçalves AR, Aeby S, Pillonel T, Jacot D, Bertelli C, Greub G, Leuzinger K, Stange M, Mari A, Roloff T, Seth-Smith H, Hirsch HH, Egli A, Redondo M, Kobel O, Noppen C, du Plessis L, Beerenwinkel N, Neher RA, Beisel C, Stadler T. Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data. Sci Transl Med 2023; 15:eabn7979. [PMID: 36346321 PMCID: PMC9765449 DOI: 10.1126/scitranslmed.abn7979] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020-the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland's border closures decoupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 to 98% reduction in introductions during Switzerland's strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred using a phylodynamic model. We found that transmission slowed 35 to 63% upon outbreak detection in summer 2020 but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.
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Affiliation(s)
- Sarah A. Nadeau
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Corresponding author. (T.S.); (S.A.N.)
| | - Timothy G. Vaughan
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | | | - Ivan Topolsky
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Chaoran Chen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Emma Hodcroft
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Institute for Social and Preventive Medicine, University of Bern; 3012, Bern, Switzerland
| | - Tobias Schär
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Ina Nissen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Natascha Santacroce
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Elodie Burcklen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Pedro Ferreira
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Kim Philipp Jablonski
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Susana Posada-Céspedes
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Vincenzo Capece
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Sophie Seidel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | | | - Julia M. Martinez-Gomez
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Phil Cheng
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Philipp P. Bosshard
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Mitchell P. Levesque
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Verena Kufner
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Stefan Schmutz
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Maryam Zaheri
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Michael Huber
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Alexandra Trkola
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Samuel Cordey
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals & Faculty of Medicine; 1205, Geneva, Switzerland
| | - Florian Laubscher
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals & Faculty of Medicine; 1205, Geneva, Switzerland
| | - Ana Rita Gonçalves
- Swiss National Reference Centre for Influenza, University Hospitals of Geneva; 1205, Geneva, Switzerland
| | - Sébastien Aeby
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Trestan Pillonel
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Damien Jacot
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Claire Bertelli
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Gilbert Greub
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Karoline Leuzinger
- Division of Clinical Virology, University Hospital Basel; 4051, Basel, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland
| | - Madlen Stange
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Alfredo Mari
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Tim Roloff
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Helena Seth-Smith
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Hans H. Hirsch
- Division of Clinical Virology, University Hospital Basel; 4051, Basel, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland
| | - Adrian Egli
- Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | | | | | | | - Louis du Plessis
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Richard A. Neher
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Biozentrum, University of Basel; 4056, Basel, Switzerland
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Corresponding author. (T.S.); (S.A.N.)
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6
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Zhao S, Hu I, Lou J, Chong MK, Cao L, He D, Zee BC, Wang MH. The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale. Infect Dis Model 2022; 8:107-121. [PMID: 36632179 PMCID: PMC9811219 DOI: 10.1016/j.idm.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/19/2022] [Accepted: 12/25/2022] [Indexed: 12/28/2022] Open
Abstract
Virus evolution is a common process of pathogen adaption to host population and environment. Frequently, a small but important fraction of virus mutations are reported to contribute to higher risks of host infection, which is one of the major determinants of infectious diseases outbreaks at population scale. The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly. Based on classic epidemiology theories of disease transmission, we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population. The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness. The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England. The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population.
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Affiliation(s)
- Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Inchi Hu
- Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Hong Kong, China
| | - Jingzhi Lou
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | - Marc K.C. Chong
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Lirong Cao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Benny C.Y. Zee
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Maggie H. Wang
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China,CUHK Shenzhen Research Institute, Shenzhen, China,Corresponding author. JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
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7
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Wells K, Flynn R. Managing host-parasite interactions in humans and wildlife in times of global change. Parasitol Res 2022; 121:3063-3071. [PMID: 36066742 PMCID: PMC9446624 DOI: 10.1007/s00436-022-07649-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/30/2022] [Indexed: 11/24/2022]
Abstract
Global change in the Anthropocene has modified the environment of almost any species on earth, be it through climate change, habitat modifications, pollution, human intervention in the form of mass drug administration (MDA), or vaccination. This can have far-reaching consequences on all organisational levels of life, including eco-physiological stress at the cell and organism level, individual fitness and behaviour, population viability, species interactions and biodiversity. Host-parasite interactions often require highly adapted strategies by the parasite to survive and reproduce within the host environment and ensure efficient transmission among hosts. Yet, our understanding of the system-level outcomes of the intricate interplay of within host survival and among host parasite spread is in its infancy. We shed light on how global change affects host-parasite interactions at different organisational levels and address challenges and opportunities to work towards better-informed management of parasite control. We argue that global change affects host-parasite interactions in wildlife inhabiting natural environments rather differently than in humans and invasive species that benefit from anthropogenic environments as habitat and more deliberate rather than erratic exposure to therapeutic drugs and other control efforts.
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Affiliation(s)
- Konstans Wells
- Department of Biosciences, Swansea University, Swansea, SA28PP, UK.
| | - Robin Flynn
- Graduate Studies Office, South East Technological University, Cork Road Campus, Waterford, X91 K0EK, Ireland
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8
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Chen C, Nadeau S, Topolsky I, Beerenwinkel N, Stadler T. Advancing genomic epidemiology by addressing the bioinformatics bottleneck: Challenges, design principles, and a Swiss example. Epidemics 2022; 39:100576. [PMID: 35605437 PMCID: PMC9107180 DOI: 10.1016/j.epidem.2022.100576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/05/2022] [Accepted: 05/05/2022] [Indexed: 11/28/2022] Open
Abstract
The SARS-CoV-2 pandemic led to a huge increase in global pathogen genome sequencing efforts, and the resulting data are becoming increasingly important to detect variants of concern, monitor outbreaks, and quantify transmission dynamics. However, this rapid up-scaling in data generation brought with it many IT infrastructure challenges. In this paper, we report about developing an improved system for genomic epidemiology. We (i) highlight key challenges that were exacerbated by the pandemic situation, (ii) provide data infrastructure design principles to address them, and (iii) give an implementation example developed by the Swiss SARS-CoV-2 Sequencing Consortium (S3C) in response to the COVID-19 pandemic. Finally, we discuss remaining challenges to data infrastructure for genomic epidemiology. Improving these infrastructures will help better detect, monitor, and respond to future public health threats.
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Affiliation(s)
- Chaoran Chen
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Sarah Nadeau
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Ivan Topolsky
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
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9
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Nikolaeva A, Versnel J. Analytical observational study evaluating global pandemic preparedness and the effectiveness of early COVID-19 responses in Ethiopia, Nigeria, Singapore, South Korea, Sweden, Taiwan, UK and USA. BMJ Open 2022; 12:e053374. [PMID: 35110318 PMCID: PMC8811275 DOI: 10.1136/bmjopen-2021-053374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 12/17/2021] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES An analysis of early country-specific COVID-19 strategies and the impact of policies, healthcare resources and cultural influences on their effectiveness. DESIGN Analytical observational study. SETTING USA, UK, Sweden, South Korea, Singapore, Taiwan, Ethiopia and Nigeria. MAIN OUTCOME MEASURES OxCGRT indices were used to quantify variations in governments' responses, and effectiveness was measured by the number of deaths as a proportion of the population. Hofstede's cultural dimensions, and the availability of healthcare resources, were analysed for their potential impact on effectiveness. RESULTS Effective strategies reflect factors such as speed of governmental intervention, cultural norms, population demographics and available resources. While biases, confounders and lack of data at the beginning of the pandemic make inferences challenging, publicly available data suggest that South Korea, Singapore and Taiwan were most successful through rapid identification and isolation of cases, and effective contact tracing systems. CONCLUSION The rapid spread of the highly transmissible SARS-CoV-2 virus took many countries by surprise and the delayed global response contributed to the severity of the COVID-19 pandemic. The speed at which strategies were implemented is highly correlated to the number of deaths. Factors such as cultural norms and healthcare resources impact effectiveness significantly, implying that implementation of a global 'one size fits all' approach is challenging. Global preparedness should focus on effective surveillance and preparedness strategies to enable timely identification and containment of future threats.
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Affiliation(s)
- Alexandra Nikolaeva
- Academy of Therapeutic Sciences, Faculty of Biology, University of Cambridge, Cambridge, UK
| | - Jenny Versnel
- Academy of Therapeutic Sciences, Faculty of Biology, University of Cambridge, Cambridge, UK
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10
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Mu Y, Shao M, Zhong B, Zhao Y, Leung KMY, Giesy JP, Ma J, Wu F, Zeng F. Transmission of SARS-CoV-2 virus and ambient temperature: a critical review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:37051-37059. [PMID: 34053039 PMCID: PMC8164483 DOI: 10.1007/s11356-021-14625-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 05/25/2021] [Indexed: 06/12/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented public health, and social and economic challenges. It remains unclear whether seasonal changes in ambient temperature will alter spreading trajectory of the COVID-19 epidemic. The probable mechanism on this is still lacking. This review summarizes the most recent research data on the effect of ambient temperature on the COVID-19 epidemic characteristic. The available data suggest that (i) mesophilic traits of viruses are different due to their molecular composition; (ii) increasing ambient temperature decreases the persistence of some viruses in aquatic media; (iii) a 1°C increase in the average monthly minimum ambient temperatures (AMMAT) was related to a 0.72% fewer mammalian individuals that would be infected by coronavirus; (iv) proportion of zoonotic viruses of mammals including humans is probably related to their body temperature difference; (v) seasonal divergence between the northern and southern hemispheres may be a significant driver in determining a waved trajectory in the next 2 years. Further research is needed to understand its effects and mechanisms of global temperature change so that effective strategies can be adopted to curb its natural effects. This paper mainly explores possible scientific hypothesis and evidences that local communities and authorities should consider to find optimal solutions that can limit the transmission of SARS-CoV-2 virus.
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Affiliation(s)
- Yunsong Mu
- School of Environment & Natural Resources, Renmin University of China, No.59, Zhongguancun Street, Haidian District, Beijing, 100872, China.
| | - Meichen Shao
- School of Environment & Natural Resources, Renmin University of China, No.59, Zhongguancun Street, Haidian District, Beijing, 100872, China
| | - Buqing Zhong
- Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China
| | - Yiqun Zhao
- School of Environment & Natural Resources, Renmin University of China, No.59, Zhongguancun Street, Haidian District, Beijing, 100872, China
| | - Kenneth M Y Leung
- State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
| | - John P Giesy
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Environmental Science, Baylor University, Waco, TX, USA
| | - Jin Ma
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Fangang Zeng
- School of Environment & Natural Resources, Renmin University of China, No.59, Zhongguancun Street, Haidian District, Beijing, 100872, China.
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11
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Giandhari J, Pillay S, Wilkinson E, Tegally H, Sinayskiy I, Schuld M, Lourenço J, Chimukangara B, Lessells R, Moosa Y, Gazy I, Fish M, Singh L, Sedwell Khanyile K, Fonseca V, Giovanetti M, Carlos Junior Alcantara L, Petruccione F, de Oliveira T. Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report. Int J Infect Dis 2021; 103:234-241. [PMID: 33189939 PMCID: PMC7658561 DOI: 10.1016/j.ijid.2020.11.128] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 11/02/2020] [Accepted: 11/05/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES The Network for Genomic Surveillance in South Africa (NGS-SA) was formed to investigate the introduction and understand the early transmission dynamics of the SARS-CoV-2 epidemic in South-Africa. DESIGN This paper presents the first results from this group, which is a molecular epidemiological study of the first 21 SARS-CoV-2 whole genomes sampled in the first port of entry - KwaZulu-Natal (KZN) - during the first month of the epidemic. By combining this with calculations of the effective reproduction number (R), it aimed to shed light on the patterns of infections in South Africa. RESULTS Two of the largest provinces - Gauteng and KZN - had a slow growth rate for the number of detected cases, while the epidemic spread faster in the Western Cape and Eastern Cape. The estimates of transmission potential suggested a decrease towards R = 1 since the first cases and deaths, but a subsequent estimated R average of 1.39 between 6-18 May 2020. It was also demonstrated that early transmission in KZN was associated with multiple international introductions and dominated by lineages B1 and B. Evidence for locally acquired infections in a hospital in Durban within the first month of the epidemic was also provided. CONCLUSION The COVID-19 pandemic in South Africa was very heterogeneous in its spatial dimension, with many distinct introductions of SARS-CoV2 in KZN and evidence of nosocomial transmission, which inflated early mortality in KZN. The epidemic at the local level was still developing and NGS-SA aimed to clarify the dynamics in South Africa and devise the most effective measures as the outbreak evolved.
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Affiliation(s)
- Jennifer Giandhari
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Sureshnee Pillay
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Eduan Wilkinson
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Ilya Sinayskiy
- Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, Durban, South Africa; National Institute for Theoretical Physics (NITheP), KwaZulu-Natal, South Africa
| | - Maria Schuld
- Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, Durban, South Africa
| | - José Lourenço
- Department of Zoology, University of Oxford, Oxford, UK
| | - Benjamin Chimukangara
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Richard Lessells
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa; Department of Zoology, University of Oxford, Oxford, UK
| | - Yunus Moosa
- Department of Zoology, University of Oxford, Oxford, UK
| | - Inbal Gazy
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Maryam Fish
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Lavanya Singh
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Khulekani Sedwell Khanyile
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Vagner Fonseca
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa; Laboratorio de Genetica Celular e Molecular, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Laboratório de Flavivírus, Instituto Oswaldo Cruz Fiocruz, Rio de Janeiro, Brazil
| | - Marta Giovanetti
- Laboratório de Flavivírus, Instituto Oswaldo Cruz Fiocruz, Rio de Janeiro, Brazil
| | - Luiz Carlos Junior Alcantara
- Laboratorio de Genetica Celular e Molecular, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Laboratório de Flavivírus, Instituto Oswaldo Cruz Fiocruz, Rio de Janeiro, Brazil
| | - Francesco Petruccione
- Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, Durban, South Africa; National Institute for Theoretical Physics (NITheP), KwaZulu-Natal, South Africa
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa; Centre for Aids Programme of Research in South Africa (CAPRISA), Durban, South Africa; Department of Global Health, University of Washington, Seattle, Washington, USA.
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12
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Komissarov AB, Safina KR, Garushyants SK, Fadeev AV, Sergeeva MV, Ivanova AA, Danilenko DM, Lioznov D, Shneider OV, Shvyrev N, Spirin V, Glyzin D, Shchur V, Bazykin GA. Genomic epidemiology of the early stages of the SARS-CoV-2 outbreak in Russia. Nat Commun 2021; 12:649. [PMID: 33510171 PMCID: PMC7844267 DOI: 10.1038/s41467-020-20880-z] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/17/2020] [Indexed: 01/19/2023] Open
Abstract
The ongoing pandemic of SARS-CoV-2 presents novel challenges and opportunities for the use of phylogenetics to understand and control its spread. Here, we analyze the emergence of SARS-CoV-2 in Russia in March and April 2020. Combining phylogeographic analysis with travel history data, we estimate that the sampled viral diversity has originated from at least 67 closely timed introductions into Russia, mostly in late February to early March. All but one of these introductions were not from China, suggesting that border closure with China has helped delay establishment of SARS-CoV-2 in Russia. These introductions resulted in at least 9 distinct Russian lineages corresponding to domestic transmission. A notable transmission cluster corresponded to a nosocomial outbreak at the Vreden hospital in Saint Petersburg; phylodynamic analysis of this cluster reveals multiple (2-3) introductions each giving rise to a large number of cases, with a high initial effective reproduction number of 3.0 [1.9, 4.3].
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Affiliation(s)
| | - Ksenia R Safina
- Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia.,A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow, Russia
| | - Sofya K Garushyants
- A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow, Russia
| | - Artem V Fadeev
- Smorodintsev Research Institute of Influenza, Saint Petersburg, Russia
| | - Mariia V Sergeeva
- Smorodintsev Research Institute of Influenza, Saint Petersburg, Russia
| | - Anna A Ivanova
- Smorodintsev Research Institute of Influenza, Saint Petersburg, Russia
| | - Daria M Danilenko
- Smorodintsev Research Institute of Influenza, Saint Petersburg, Russia
| | - Dmitry Lioznov
- Smorodintsev Research Institute of Influenza, Saint Petersburg, Russia.,First Pavlov State Medical University, Saint Petersburg, Russia
| | - Olga V Shneider
- Vreden Russian Research Institute of Traumatology and Orthopaedics, Saint Petersburg, Russia
| | - Nikita Shvyrev
- National Research University Higher School of Economics, Moscow, Russia
| | - Vadim Spirin
- National Research University Higher School of Economics, Moscow, Russia
| | - Dmitry Glyzin
- National Research University Higher School of Economics, Moscow, Russia
| | - Vladimir Shchur
- National Research University Higher School of Economics, Moscow, Russia
| | - Georgii A Bazykin
- Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia. .,A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow, Russia.
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13
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Marghalani AM, Althumali IM, Yousef LM, Alharthi MA, Alahmari ZS, Kabel AM. Coronavirus disease 2019 (COVID-19): Insights into the recent trends and the role of the primary care in diabetic patients. J Family Med Prim Care 2020; 9:3843-3847. [PMID: 33110777 PMCID: PMC7586637 DOI: 10.4103/jfmpc.jfmpc_683_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 06/10/2020] [Accepted: 07/01/2020] [Indexed: 01/04/2023] Open
Abstract
Diseases with viral etiology continue to emerge in the last years and may represent serious problems that affect various aspects of life. Coronaviruses are a large family of RNA viruses that cause illness affecting the respiratory tract ranging from common cold to severe respiratory distress syndrome. In the last weeks of 2019, enormous cases of unexplained pneumonia were reported in China. Few days later, a novel type of coronavirus was identified as the causative agent of these cases and the disease was named as coronavirus disease 2019 (COVID-19) by the World Health Organization. The disease was rapidly spreading in China and all over the world and now it is considered as pandemic catastrophe. It can be transmitted from animals to human and from human to human. Diabetes mellitus may represent a potential risk factor for the development of COVID-19, possibly due to the relative state of immunosuppression frequently encountered in diabetic patients. This review sheds light on COVID-19 based on the currently available data with reference to the role of the primary care in diabetic patients.
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Affiliation(s)
| | | | | | | | | | - Ahmed M Kabel
- Department of Clinical Pharmacy, College of Pharmacy, Taif University, Taif, KSA.,Department of Pharmacology, Faculty of Medicine, Tanta University, Tanta, Egypt
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14
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Villabona-Arenas CJ, Hanage WP, Tully DC. Phylogenetic interpretation during outbreaks requires caution. Nat Microbiol 2020; 5:876-877. [PMID: 32427978 PMCID: PMC8168400 DOI: 10.1038/s41564-020-0738-5] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
How viruses are related, and how they have evolved and spread over time, can be investigated using phylogenetics. Here, we set out how genomic analyses should be used during an epidemic and propose that phylogenetic insights from the early stages of an outbreak should heed all of the available epidemiological information.
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Affiliation(s)
- Ch Julián Villabona-Arenas
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Damien C Tully
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
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15
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Giandhari J, Pillay S, Wilkinson E, Tegally H, Sinayskiy I, Schuld M, Lourenco J, Chimukangara B, Lessells R, Moosa Y, Gazy I, Fish M, Singh L, Khanyile KS, Fonseca V, Giovanetti M, Alcantara LC, Petruccione F, de Oliveira T. Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.05.29.20116376. [PMID: 32511505 PMCID: PMC7273273 DOI: 10.1101/2020.05.29.20116376] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background The emergence of a novel coronavirus, SARS-CoV-2, in December 2019, progressed to become a world pandemic in a few months and reached South Africa at the beginning of March. To investigate introduction and understand the early transmission dynamics of the virus, we formed the South African Network for Genomics Surveillance of COVID (SANGS_COVID), a network of ten government and university laboratories. Here, we present the first results of this effort, which is a molecular epidemiological study of the first twenty-one SARS-CoV-2 whole genomes sampled in the first port of entry, KwaZulu-Natal (KZN), during the first month of the epidemic. By combining this with calculations of the effective reproduction number (R), we aim to shed light on the patterns of infections that define the epidemic in South Africa. Methods R was calculated using positive cases and deaths from reports provided by the four major provinces. Molecular epidemiology investigation involved sequencing viral genomes from patients in KZN using ARCTIC protocols and assembling whole genomes using meticulous alignment methods. Phylogenetic analysis was performed using maximum likelihood (ML) and Bayesian trees, lineage classification and molecular clock calculations. Findings The epidemic in South Africa has been very heterogeneous. Two of the largest provinces, Gauteng, home of the two large metropolis Johannesburg and Pretoria, and KwaZulu-Natal, home of the third largest city in the country Durban, had a slow growth rate on the number of detected cases. Whereas, Western Cape, home of Cape Town, and the Eastern Cape provinces the epidemic is spreading fast. Our estimates of transmission potential for South Africa suggest a decreasing transmission potential towards R=1 since the first cases and deaths have been reported. However, between 06 May and 18 May 2020, we estimate that R was on average 1.39 (1.04 - 2.15, 95% CI). We also demonstrate that early transmission in KZN, and most probably in all main regions of SA, was associated with multiple international introductions and dominated by lineages B1 and B. The study also provides evidence for locally acquired infections in a hospital in Durban within the first month of the epidemic, which inflated early mortality in KZN. Interpretation This first report of SANGS_COVID consortium focuses on understanding the epidemic heterogeneity and introduction of SARS-CoV-2 strains in the first month of the epidemic in South Africa. The early introduction of SARS-CoV-2 in KZN included caused a localized outbreak in a hospital, provides potential explanations for the initially high death rates in the province. The current high rate of transmission of COVID-19 in the Western Cape and Eastern Cape highlights the crucial need to strength local genomic surveillance in South Africa. Funding UKZN Flagship Program entitled: Afrocentric Precision Approach to Control Health Epidemic, by a research Flagship grant from the South African Medical Research Council (MRC-RFA-UFSP-01-2013/UKZN HIVEPI, by the the Technology Innovation Agency and the the Department of Science and Innovation and by National Human Genome Re- search Institute of the National Institutes of Health under Award Number U24HG006941. H3ABioNet is an initiative of the Human Health and Heredity in Africa Consortium (H3Africa).
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Affiliation(s)
- Jennifer Giandhari
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Sureshnee Pillay
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Eduan Wilkinson
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Ilya Sinayskiy
- Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, Durban, South Africa
- National Institute for Theoretical Physics (NITheP), KwaZulu-Natal, 4001, South Africa
| | - Maria Schuld
- Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, Durban, South Africa
| | - Jose Lourenco
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Benjamin Chimukangara
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Richard Lessells
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
- Infectious Diseases Department, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Yunus Moosa
- Infectious Diseases Department, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Inbal Gazy
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Maryam Fish
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Lavanya Singh
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Khulekani Sedwell Khanyile
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Vagner Fonseca
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
- Laboratorio de Genetica Celular e Molecular, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório de Flavivírus, Instituto Oswaldo Cruz Fiocruz, Rio de Janeiro, Brazil
| | - Marta Giovanetti
- Laboratório de Flavivírus, Instituto Oswaldo Cruz Fiocruz, Rio de Janeiro, Brazil
| | - Luiz Carols Alcantara
- Laboratorio de Genetica Celular e Molecular, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório de Flavivírus, Instituto Oswaldo Cruz Fiocruz, Rio de Janeiro, Brazil
| | - Francesco Petruccione
- Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, Durban, South Africa
- National Institute for Theoretical Physics (NITheP), KwaZulu-Natal, 4001, South Africa
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for Aids Programme of Research in South Africa (CAPRISA), Durban South Africa
- Department of Global Health, University of Washington, Seattle, Washington, USA
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16
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Candido DDS, Watts A, Abade L, Kraemer MUG, Pybus OG, Croda J, de Oliveira W, Khan K, Sabino EC, Faria NR. Routes for COVID-19 importation in Brazil. J Travel Med 2020; 27:5809508. [PMID: 32211799 PMCID: PMC7184379 DOI: 10.1093/jtm/taaa042] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 03/13/2020] [Accepted: 03/16/2020] [Indexed: 11/15/2022]
Abstract
The global outbreak caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been declared a pandemic by the World Health Organization. As the number of imported SARS-CoV-2 cases is on the rise in Brazil, we use incidence and historical air travel data to estimate the most important routes of importation into the country.
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Affiliation(s)
| | - Alexander Watts
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.,Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Leandro Abade
- Department of Zoology, University of Oxford, Oxford, UK
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.,Harvard Medical School, Harvard University, Boston, MA, USA.,Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, USA
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.,Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK
| | - Julio Croda
- Secretaria de Vigilância em Saúde, Coordenação Geral de Laboratórios de Saúde Pública, Ministério da Saúde, Brasília, Brazil.,Laboratório de Pesquisa em Ciências da Saúde, Universidade Federal da Grande Dourados, Dourados Mato Grosso do Sul, Brazil.,Fundação Oswaldo Cruz Campo Grande, Campo Grande, Brazil
| | - Wanderson de Oliveira
- Secretaria de Vigilância em Saúde, Coordenação Geral de Laboratórios de Saúde Pública, Ministério da Saúde, Brasília, Brazil
| | - Kamran Khan
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.,Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Ester C Sabino
- Instituto de Medicina Tropical, University of São Paulo, São Paulo, Brazil
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK.,Instituto de Medicina Tropical, University of São Paulo, São Paulo, Brazil.,Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London
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17
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Kahn R, Peak CM, Fernández-Gracia J, Hill A, Jambai A, Ganda L, Castro MC, Buckee CO. Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone. Proc Natl Acad Sci U S A 2020; 117:5067-5073. [PMID: 32054785 PMCID: PMC7060667 DOI: 10.1073/pnas.1913052117] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen's underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen's incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.
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Affiliation(s)
- Rebecca Kahn
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Corey M Peak
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Juan Fernández-Gracia
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
- Institute for Cross-Disciplinary Physics and Complex Systems, Universitat de les Illes Balears - Consell Superior d'Investigacions Científiques, E-07122 Palma de Mallorca, Spain
| | - Alexandra Hill
- Disease Control in Humanitarian Emergencies, World Health Organization, CH-1211 Geneva 27, Switzerland
| | - Amara Jambai
- Disease Control and Prevention, Sierra Leone Ministry of Health and Sanitation, Freetown, Sierra Leone FPGG+89
| | - Louisa Ganda
- Country Office, World Health Organization, Freetown, Sierra Leone FPGG+89
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115;
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18
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Zhao S, Musa SS, Hebert JT, Cao P, Ran J, Meng J, He D, Qin J. Modelling the effective reproduction number of vector-borne diseases: the yellow fever outbreak in Luanda, Angola 2015-2016 as an example. PeerJ 2020; 8:e8601. [PMID: 32149023 PMCID: PMC7049463 DOI: 10.7717/peerj.8601] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 01/19/2020] [Indexed: 01/02/2023] Open
Abstract
The burden of vector-borne diseases (Dengue, Zika virus, yellow fever, etc.) gradually increased in the past decade across the globe. Mathematical modelling on infectious diseases helps to study the transmission dynamics of the pathogens. Theoretically, the diseases can be controlled and eventually eradicated by maintaining the effective reproduction number, (R eff ), strictly less than 1. We established a vector-host compartmental model, and derived (R eff ) for vector-borne diseases. The analytic form of the (R eff ) was found to be the product of the basic reproduction number and the geometric average of the susceptibilities of the host and vector populations. The (R eff ) formula was demonstrated to be consistent with the estimates of the 2015-2016 yellow fever outbreak in Luanda, and distinguished the second minor epidemic wave. For those using the compartmental model to study the vector-borne infectious disease epidemics, we further remark that it is important to be aware of whether one or two generations is considered for the transition "from host to vector to host" in reproduction number calculation.
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Affiliation(s)
- Shi Zhao
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
- Division of Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
- Clinical Trials and Biostatistics Lab, Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen, China
| | - Salihu S. Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Jay T. Hebert
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Peihua Cao
- Department of Hepatobiliary Surgery II, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jinjun Ran
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Jiayi Meng
- School of Economics and Finance, Xi’an International Studies University, Xi’an, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Jing Qin
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
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19
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Disease Resurgence, Production Capability Issues and Safety Concerns in the Context of an Aging Population: Is There a Need for a New Yellow Fever Vaccine? Vaccines (Basel) 2019; 7:vaccines7040179. [PMID: 31717289 PMCID: PMC6963298 DOI: 10.3390/vaccines7040179] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 10/28/2019] [Accepted: 11/05/2019] [Indexed: 12/19/2022] Open
Abstract
Yellow fever is a potentially fatal, mosquito-borne viral disease that appears to be experiencing a resurgence in endemic areas in Africa and South America and spreading to non-endemic areas despite an effective vaccine. This trend has increased the level of concern about the disease and the potential for importation to areas in Asia with ecological conditions that can sustain yellow fever virus transmission. In this article, we provide a broad overview of yellow fever burden of disease, natural history, treatment, vaccine, prevention and control initiatives, and vaccine and therapeutic agent development efforts.
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20
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Genomic analysis of respiratory syncytial virus infections in households and utility in inferring who infects the infant. Sci Rep 2019; 9:10076. [PMID: 31296922 PMCID: PMC6624209 DOI: 10.1038/s41598-019-46509-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 06/26/2019] [Indexed: 12/18/2022] Open
Abstract
Infants (under 1-year-old) are at most risk of life threatening respiratory syncytial virus (RSV) disease. RSV epidemiological data alone has been insufficient in defining who acquires infection from whom (WAIFW) within households. We investigated RSV genomic variation within and between infected individuals and assessed its potential utility in tracking transmission in households. Over an entire single RSV season in coastal Kenya, nasal swabs were collected from members of 20 households every 3-4 days regardless of symptom status and screened for RSV nucleic acid. Next generation sequencing was used to generate >90% RSV full-length genomes for 51.1% of positive samples (191/374). Single nucleotide polymorphisms (SNPs) observed during household infection outbreaks ranged from 0-21 (median: 3) while SNPs observed during single-host infection episodes ranged from 0-17 (median: 1). Using the viral genomic data alone there was insufficient resolution to fully reconstruct within-household transmission chains. For households with clear index cases, the most likely source of infant infection was via a toddler (aged 1 to <3 years-old) or school-aged (aged 6 to <12 years-old) co-occupant. However, for best resolution of WAIFW within households, we suggest an integrated analysis of RSV genomic and epidemiological data.
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21
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Kraemer MUG, Golding N, Bisanzio D, Bhatt S, Pigott DM, Ray SE, Brady OJ, Brownstein JS, Faria NR, Cummings DAT, Pybus OG, Smith DL, Tatem AJ, Hay SI, Reiner RC. Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings. Sci Rep 2019; 9:5151. [PMID: 30914669 PMCID: PMC6435716 DOI: 10.1038/s41598-019-41192-3] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 03/03/2019] [Indexed: 12/03/2022] Open
Abstract
Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014-16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD's incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.
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Affiliation(s)
- M U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.
- Harvard Medical School, Boston, MA, USA.
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
| | - N Golding
- Department of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - D Bisanzio
- RTI International, Washington, D.C., USA
- Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - S Bhatt
- Imperial College London, London, United Kingdom
| | - D M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - S E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - O J Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - J S Brownstein
- Harvard Medical School, Boston, MA, USA
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
| | - N R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - D A T Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - O G Pybus
- Department of Zoology, University of Oxford, Oxford, UK
| | - D L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Sanaria Institute for Global Health and Tropical Medicine, Rockville, USA
| | - A J Tatem
- WorldPop, Department of Geography and Environmental Sciences, University of Southampton, Southampton, UK
- Flowminder Foundation, Stockholm, Sweden
| | - S I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
| | - R C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
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22
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Kamvar ZN, Cai J, Pulliam JRC, Schumacher J, Jombart T. Epidemic curves made easy using the R package incidence. F1000Res 2019; 8:139. [PMID: 31119031 PMCID: PMC6509961 DOI: 10.12688/f1000research.18002.1] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/22/2019] [Indexed: 11/20/2022] Open
Abstract
The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling. incidence is available from https://www.repidemicsconsortium.org/incidence.
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Affiliation(s)
- Zhian N Kamvar
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology , School of Public Health, Imperial College London, London, UK
| | - Jun Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Juliet R C Pulliam
- South African DST-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA),, Stellenbosch University, Stellenbosch, South Africa
| | | | - Thibaut Jombart
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology , School of Public Health, Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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23
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Perkins TA. Letter to the editor in response to 'Reconstruction and prediction of viral disease epidemics'. Epidemiol Infect 2019; 147:e98. [PMID: 30869030 PMCID: PMC6518571 DOI: 10.1017/s095026881900013x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- T. Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, 100 Galvin Hall, Notre Dame, IN 46556, USA
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