1
|
Perofsky AC, Huddleston J, Hansen C, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Lewis N, Whittaker L, Ermetal B, Harvey R, Galiano M, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Sullivan SG, Barr IG, Subbarao K, Krammer F, Bedford T, Viboud C. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.02.23296453. [PMID: 37873362 PMCID: PMC10593063 DOI: 10.1101/2023.10.02.23296453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.
Collapse
Affiliation(s)
- Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
| | - Chelsea Hansen
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
| | - John R Barnes
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Thomas Rowe
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Xiyan Xu
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Rebecca Kondor
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - David E Wentworth
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Nicola Lewis
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Lynne Whittaker
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Burcu Ermetal
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Ruth Harvey
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Monica Galiano
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Rodney Stuart Daniels
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - John W McCauley
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Seiichiro Fujisaki
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Kazuya Nakamura
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Noriko Kishida
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Shinji Watanabe
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Hideki Hasegawa
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Ian G Barr
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Kanta Subbarao
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Florian Krammer
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, United States
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, United States
| | - Trevor Bedford
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
- Department of Genome Sciences, University of Washington, United States
- Howard Hughes Medical Institute, Seattle, United States
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, United States
| |
Collapse
|
2
|
Du Z, Shao Z, Zhang X, Chen R, Chen T, Bai Y, Wang L, Lau EHY, Cowling BJ. Nowcasting and Forecasting Seasonal Influenza Epidemics - China, 2022-2023. China CDC Wkly 2023; 5:1100-1106. [PMID: 38125915 PMCID: PMC10728554 DOI: 10.46234/ccdcw2023.206] [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: 07/16/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
Background Seasonal influenza resurged in China in February 2023, causing a large number of hospitalizations. While influenza epidemics occurred across China during the coronavirus disease 2019 (COVID-19) pandemic, the relaxation of COVID-19 containment measures in December 2022 may have contributed to the spread of acute respiratory infections in winter 2022/2023. Methods Using a mathematical model incorporating influenza activity as measured by influenza-like illness (ILI) data for northern and southern regions of China, we reconstructed the seasonal influenza incidence from October 2015 to September 2019 before the COVID-19 pandemic. Using this trained model, we predicted influenza activities in northern and southern China from March to September 2023. Results We estimated the effective reproduction number R e as 1.08 [95% confidence interval ( CI): 0.51, 1.65] in northern China and 1.10 (95% CI: 0.55, 1.67) in southern China at the start of the 2022-2023 influenza season. We estimated the infection attack rate of this influenza wave as 18.51% (95% CI: 0.00%, 37.78%) in northern China and 28.30% (95% CI: 14.77%, 41.82%) in southern China. Conclusions The 2023 spring wave of seasonal influenza in China spread until July 2023 and infected a substantial number of people.
Collapse
Affiliation(s)
- Zhanwei Du
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Zengyang Shao
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Xiao Zhang
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Ruohan Chen
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yuan Bai
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Eric H. Y. Lau
- Institute for Health Transformation & School of Health & Social Development, Deakin University, Melbourne, Australia
| | - Benjamin J. Cowling
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| |
Collapse
|
3
|
Zheng X, Zhao K, Xu X, Deng Y, Leung K, Wu JT, Leung GM, Peiris M, Poon LLM, Zhang T. Development and application of influenza virus wastewater surveillance in Hong Kong. WATER RESEARCH 2023; 245:120594. [PMID: 37741039 DOI: 10.1016/j.watres.2023.120594] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/03/2023] [Accepted: 09/06/2023] [Indexed: 09/25/2023]
Abstract
Wastewater surveillance is considered as a powerful tool in providing cost-effective, population-wide and near real-time surveillance results for controlling infectious diseases (i.e., SARS-CoV-2, influenza virus), complementary to clinical surveillance. To facilitate the utility of this emerging tool, we developed two preanalytical protocols (supernatant-based and pellet-based) for influenza A/B virus (IAV/IBV) wastewater surveillance and applied them to the established wastewater surveillance network for large-scale longitudinal monitoring in Hong Kong. We tested 724 wastewater samples from 24 stationary sites for weekly surveillance for 8 months and 458 wastewater samples from 11 wastewater treatment plants (WWTPs) for more frequent (three times per week) city-wide surveillance for 4 months when influenza season commenced. We found the city-wide IAV virus concentration in wastewater were associated with the detection rate and influenza-like illness plus rates (ILI+) of clinical respiratory specimens and increased significantly after the cancelling of mask mandate that was in place for COVID-19. IBV was at low detection rates and low virus concentration levels, consistent with the low detection rates observed by clinical surveillance. In addition, we conducted virus subtype identification in selected wastewater samples, and observed the H1pdm was the major circulation subtype. Moreover, the obtained virus signals were confirmed by Sanger sequencing of PCR products, suggesting the feasibility and applicability of established methods for rapid detection of influenza virus types and subtypes in wastewater surveillance. This study demonstrates the applicability of IAV/IBV wastewater surveillance to current wastewater infrastructures and it could be used as a rapid and cost-effective surveillance strategy to track virus transmission patterns in the community for timely public health actions in the future.
Collapse
Affiliation(s)
- Xiawan Zheng
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Keyue Zhao
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Xiaoqing Xu
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Yu Deng
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; Laboratory of Data Discovery for Health Limited (D(2)4H), Hong Kong Science Park, Hong Kong SAR, China; The University of Hong Kong - Shenzhen Hospital, Shenzhen, China
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; Laboratory of Data Discovery for Health Limited (D(2)4H), Hong Kong Science Park, Hong Kong SAR, China; The University of Hong Kong - Shenzhen Hospital, Shenzhen, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; Laboratory of Data Discovery for Health Limited (D(2)4H), Hong Kong Science Park, Hong Kong SAR, China
| | - Malik Peiris
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; HKU-Pasteur Research Pole, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Leo L M Poon
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; HKU-Pasteur Research Pole, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau SAR, China.
| |
Collapse
|
4
|
Cascante-Vega J, Galanti M, Schley K, Pei S, Shaman J. Inference of transmission dynamics and retrospective forecast of invasive meningococcal disease. PLoS Comput Biol 2023; 19:e1011564. [PMID: 37889910 PMCID: PMC10655980 DOI: 10.1371/journal.pcbi.1011564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 11/17/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023] Open
Abstract
The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.
Collapse
Affiliation(s)
- Jaime Cascante-Vega
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Marta Galanti
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | | | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
- Columbia Climate School, Columbia University, New York, New York, United States of America
| |
Collapse
|
5
|
Zhang R, Tai J, Pei S. Ensemble inference of unobserved infections in networks using partial observations. PLoS Comput Biol 2023; 19:e1011355. [PMID: 37549190 PMCID: PMC10434926 DOI: 10.1371/journal.pcbi.1011355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 08/17/2023] [Accepted: 07/12/2023] [Indexed: 08/09/2023] Open
Abstract
Undetected infections fuel the dissemination of many infectious agents. However, identification of unobserved infectious individuals remains challenging due to limited observations of infections and imperfect knowledge of key transmission parameters. Here, we use an ensemble Bayesian inference method to infer unobserved infections using partial observations. The ensemble inference method can represent uncertainty in model parameters and update model states using all ensemble members collectively. We perform extensive experiments in both model-generated and real-world networks in which individuals have differential but unknown transmission rates. The ensemble method outperforms several alternative approaches for a variety of network structures and observation rates, despite that the model is mis-specified. Additionally, the computational complexity of this algorithm scales almost linearly with the number of nodes in the network and the number of observations, respectively, exhibiting the potential to apply to large-scale networks. The inference method may support decision-making under uncertainty and be adapted for use for other dynamical models in networks.
Collapse
Affiliation(s)
- Renquan Zhang
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Jilei Tai
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| |
Collapse
|
6
|
Xiao Y, Zhou J, Cheng Q, Yang J, Chen B, Zhang T, Xu L, Xu B, Ren Z, Liu Z, Shen C, Wang C, Liu H, Li X, Li R, Yu L, Guan D, Zhang W, Wang J, Hou L, Deng K, Bai Y, Xu B, Dou D, Gong P. Global age-structured spatial modeling for emerging infectious diseases like COVID-19. PNAS NEXUS 2023; 2:pgad127. [PMID: 37143866 PMCID: PMC10153731 DOI: 10.1093/pnasnexus/pgad127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/27/2023] [Accepted: 03/30/2023] [Indexed: 05/06/2023]
Abstract
Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.
Collapse
Affiliation(s)
- Yixiong Xiao
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Jingbo Zhou
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Qu Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jun Yang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Bin Chen
- Division of Landscape Architecture, The University of Hong Kong, Hong Kong 999007, China
| | - Tao Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Bo Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Zhehao Ren
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Zhaoyang Liu
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Chong Shen
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Che Wang
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Han Liu
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Xiaoting Li
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Ruiyun Li
- School of Public Health (SPH), Nanjing Medical University, Nanjing 211166, China
| | - Le Yu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Dabo Guan
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Wusheng Zhang
- Department of Computer Science and Technology, Institute of High Performance Computing, Tsinghua University, Beijing 100084, China
| | - Jie Wang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
- AI for Earth Laboratory, Cross-Strait Institute, Tsinghua University, Beijing 100084, China
| | - Lin Hou
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Ke Deng
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Yuqi Bai
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Bing Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Dejing Dou
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Peng Gong
- To whom correspondence should be addressed:
| |
Collapse
|
7
|
Zhang R, Wang Y, Lv Z, Pei S. Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread. BMC Infect Dis 2022; 22:648. [PMID: 35896977 PMCID: PMC9326419 DOI: 10.1186/s12879-022-07636-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 07/19/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND During the early stage of the COVID-19 pandemic, many countries implemented non-pharmaceutical interventions (NPIs) to control the transmission of SARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, stay-at-home and quarantine measures were widely adopted and enforced. Understanding the effectiveness of stay-at-home and quarantine measures can inform decision-making and control planning during the ongoing COVID-19 pandemic and for future disease outbreaks. METHODS In this study, we use mathematical models to evaluate the impact of stay-at-home and quarantine measures on COVID-19 spread in four cities that experienced large-scale outbreaks in the spring of 2020: Wuhan, New York, Milan, and London. We develop a susceptible-exposed-infected-removed (SEIR)-type model with components of self-isolation and quarantine and couple this disease transmission model with a data assimilation method. By calibrating the model to case data, we estimate key epidemiological parameters before lockdown in each city. We further examine the impact of stay-at-home and quarantine rates on COVID-19 spread after lockdown using counterfactual model simulations. RESULTS Results indicate that self-isolation of susceptible population is necessary to contain the outbreak. At a given rate, self-isolation of susceptible population induced by stay-at-home orders is more effective than quarantine of SARS-CoV-2 contacts in reducing effective reproductive numbers [Formula: see text]. Variation in self-isolation and quarantine rates can also considerably affect the duration of outbreaks, attack rates and peak timing. We generate counterfactual simulations to estimate effectiveness of stay-at-home and quarantine measures. Without these two measures, the cumulative confirmed cases could be much higher than reported numbers within 40 days after lockdown in Wuhan, New York, Milan, and London. CONCLUSIONS Our findings underscore the essential role of stay-at-home orders and quarantine of SARS-CoV-2 contacts during the early phase of the pandemic.
Collapse
Affiliation(s)
- Renquan Zhang
- School of Mathematical Sciences, Dalian University of Technology, 116024 Dalian, China
| | - Yu Wang
- School of Mathematical Sciences, Dalian University of Technology, 116024 Dalian, China
| | - Zheng Lv
- School of Control Science and Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 10032 New York, USA
| |
Collapse
|
8
|
Chen Y, Liu T, Yu X, Zeng Q, Cai Z, Wu H, Zhang Q, Xiao J, Ma W, Pei S, Guo P. An ensemble forecast system for tracking dynamics of dengue outbreaks and its validation in China. PLoS Comput Biol 2022; 18:e1010218. [PMID: 35759513 PMCID: PMC9269975 DOI: 10.1371/journal.pcbi.1010218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 07/08/2022] [Accepted: 05/17/2022] [Indexed: 02/05/2023] Open
Abstract
As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011-2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.
Collapse
Affiliation(s)
- Yuliang Chen
- Department of Preventive Medicine, Shantou University Medical College, Shantou China
| | - Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xiaolin Yu
- Department of Preventive Medicine, Shantou University Medical College, Shantou China
| | - Qinghui Zeng
- Department of Preventive Medicine, Shantou University Medical College, Shantou China
| | - Zixi Cai
- Shantou Center for Disease Control and Prevention, Shantou, China
| | - Haisheng Wu
- Department of Preventive Medicine, Shantou University Medical College, Shantou China
| | - Qingying Zhang
- Department of Preventive Medicine, Shantou University Medical College, Shantou China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- * E-mail: (WM); (SP); (PG)
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States of America
- * E-mail: (WM); (SP); (PG)
| | - Pi Guo
- Department of Preventive Medicine, Shantou University Medical College, Shantou China
- * E-mail: (WM); (SP); (PG)
| |
Collapse
|
9
|
Zheng L, Qi J, Wu J, Zheng M. Changes in Influenza Activity and Circulating Subtypes During the COVID-19 Outbreak in China. Front Med (Lausanne) 2022; 9:829799. [PMID: 35391894 PMCID: PMC8980715 DOI: 10.3389/fmed.2022.829799] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 02/17/2022] [Indexed: 12/02/2022] Open
Abstract
Background Non-pharmaceutical interventions (NPIs) to mitigate COVID-19 can impact the circulation of influenza viruses. There is a need to describe the activity of influenza and its subtypes during the COVID-19 pandemic to aid in the development of influenza prevention and control measures in the next influenza season. Method Data from pathogenic surveillance performed by the Chinese National Influenza Center from January 2016 to August 2021 were extracted and stratified by type and subtype for northern China and southern China. The distribution of influenza activity and circulating subtypes were described during the COVID-19 pandemic, and data from 2016 to 2019 were used for comparisons. Results Influenza activity declined rapidly and then rose slowly during the COVID-19 pandemic in China. The distribution of influenza subtypes changed from A-dominant to B/Victoria-dominant after the COVID-19 outbreak. Discussion Whether the B/Yamagata lineage has disappeared from China deserves more attention in future virologic monitoring programs. The influenza vaccination campaign in the 2021–2022 season is an important means by which to reduce the proportion of susceptible people and limit the damage that potentially greater and earlier circulation of the virus could cause.
Collapse
Affiliation(s)
- Luyan Zheng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jinjin Qi
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jie Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Min Zheng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| |
Collapse
|
10
|
Qi Y, Shaman J, Pei S. Quantifying the Impact of COVID-19 Nonpharmaceutical Interventions on Influenza Transmission in the United States. J Infect Dis 2021; 224:1500-1508. [PMID: 34551108 PMCID: PMC8522386 DOI: 10.1093/infdis/jiab485] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Nonpharmaceutical interventions (NPIs) have been implemented to suppress transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Evidence indicates that NPIs against coronavirus disease 2019 (COVID-19) may also have effects on transmission of seasonal influenza. METHODS In this study, we use an absolute humidity-driven susceptible-infectious-recovered-susceptible (SIRS) model to quantify the reduction of influenza incidence and transmission in the United States and US Department of Health and Human Services regions after implementation of NPIs in 2020. We investigate long-term effect of NPIs on influenza incidence by projecting influenza transmission at the national scale over the next 5 years, using the SIRS model. RESULTS We estimate that incidence of influenza A/H1 and B, which circulated in early 2020, was reduced by more than 60% in the United States during the first 10 weeks following implementation of NPIs. The reduction of influenza transmission exhibits clear geographical variation. After the control measures are relaxed, potential accumulation of susceptibility to influenza infection may lead to a large outbreak, the scale of which may be affected by length of the intervention period and duration of immunity to influenza. CONCLUSIONS Healthcare systems need to prepare for potential influenza patient surges and advocate vaccination and continued precautions.
Collapse
Affiliation(s)
- Yuchen Qi
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| |
Collapse
|
11
|
Pei S, Liljeros F, Shaman J. Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings. Proc Natl Acad Sci U S A 2021; 118:e2111190118. [PMID: 34493678 PMCID: PMC8449327 DOI: 10.1073/pnas.2111190118] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 08/03/2021] [Indexed: 12/14/2022] Open
Abstract
Antimicrobial-resistant organisms (AMROs) can colonize people without symptoms for long periods of time, during which these agents can spread unnoticed to other patients in healthcare systems. The accurate identification of asymptomatic spreaders of AMRO in hospital settings is essential for supporting the design of interventions against healthcare-associated infections (HAIs). However, this task remains challenging because of limited observations of colonization and the complicated transmission dynamics occurring within hospitals and the broader community. Here, we study the transmission of methicillin-resistant Staphylococcus aureus (MRSA), a prevalent AMRO, in 66 Swedish hospitals and healthcare facilities with inpatients using a data-driven, agent-based model informed by deidentified real-world hospitalization records. Combining the transmission model, patient-to-patient contact networks, and sparse observations of colonization, we develop and validate an individual-level inference approach that estimates the colonization probability of individual hospitalized patients. For both model-simulated and historical outbreaks, the proposed method supports the more accurate identification of asymptomatic MRSA carriers than other traditional approaches. In addition, in silica control experiments indicate that interventions targeted to inpatients with a high-colonization probability outperform heuristic strategies informed by hospitalization history and contact tracing.
Collapse
Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10027;
| | - Fredrik Liljeros
- Department of Sociology, Stockholm University, 114 19 Stockholm, Sweden
- Department of Public Health Sciences, Karolinska Institutet, 171 77 Solna, Sweden
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10027;
| |
Collapse
|
12
|
Supporting Breastfeeding in 2021 and Beyond-Lessons from the Pandemic. Pediatr Rep 2021; 13:289-301. [PMID: 34205900 PMCID: PMC8293422 DOI: 10.3390/pediatric13020037] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 01/20/2023] Open
Abstract
The COVID-19 pandemic has affected maternal and infant health globally both directly from infection with the SARS-CoV-2 virus and indirectly from changes in health care resulting from social, economic, and health care policies unique to each country. The developing countries have to share the disproportionate burden on maternal and infant health. In this review, we discuss the uncertainties resulting from SARS-CoV-2 infection in pregnancy, vertical transmission of the virus, and its effects on breastfeeding of the newborn. The problems of families and communities caring for mothers with COVID-19 and its impact on breastfeeding in newborns are discussed. The challenges posed by the pandemic have forced us to think and devise innovative solutions, including telemedicine help for antenatal counseling, breastfeeding education, and lactation support. Optimal utilization of resources and technology to find creative solutions at the individual and the community level will help in facilitating maternal-infant bonding soon after birth. Appropriate health care policies to support pregnant and lactating mothers will go a long way in meeting healthy child development goals.
Collapse
|