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Gay SA, Ellison G, Xu J, Yang J, Wei Y, Wu S, Yu L, Whalen CC, Arnold J, Liu L. Phylogenetic inference of inter-population transmission rates for infectious diseases. Brief Bioinform 2024; 25:bbae312. [PMID: 38920346 PMCID: PMC11200198 DOI: 10.1093/bib/bbae312] [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: 01/17/2024] [Revised: 05/02/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
Estimating transmission rates is a challenging yet essential aspect of comprehending and controlling the spread of infectious diseases. Various methods exist for estimating transmission rates, each with distinct assumptions, data needs, and constraints. This study introduces a novel phylogenetic approach called transRate, which integrates genetic information with traditional epidemiological approaches to estimate inter-population transmission rates. The phylogenetic method is statistically consistent as the sample size (i.e. the number of pathogen genomes) approaches infinity under the multi-population susceptible-infected-recovered model. Simulation analyses indicate that transRate can accurately estimate the transmission rate with a sample size of 200 ~ 400 pathogen genomes. Using transRate, we analyzed 40,028 high-quality sequences of SARS-CoV-2 in human hosts during the early pandemic. Our analysis uncovered significant transmission between populations even before widespread travel restrictions were implemented. The development of transRate provides valuable insights for scientists and public health officials to enhance their understanding of the pandemic's progression and aiding in preparedness for future viral outbreaks. As public databases for genomic sequences continue to expand, transRate is increasingly vital for tracking and mitigating the spread of infectious diseases.
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Affiliation(s)
- Skylar A Gay
- Institute of Bioinformatics, University of Georgia, 120 Green Street, Athens, GA 30602, United States
| | - Gregory Ellison
- Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA 30602, United States
| | - Jianing Xu
- Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA 30602, United States
| | - Jialin Yang
- Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA 30602, United States
| | - Yiliang Wei
- Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, Jiangsu International Joint Center of Genomics, School of Life Sciences, Jiangsu Normal University, 101 Shanghai Road, Xuzhou, Jiangsu 221116, China
| | - Shaoyuan Wu
- Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, Jiangsu International Joint Center of Genomics, School of Life Sciences, Jiangsu Normal University, 101 Shanghai Road, Xuzhou, Jiangsu 221116, China
| | - Lili Yu
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, College of Public Health, Georgia Southern University, 1332 Southern Drive, Statesboro, GA 30677, United States
| | - Christopher C Whalen
- Global Health Institute, Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, 100 Foster Road, Athens, GA 30602, United States
| | - Jonathan Arnold
- Institute of Bioinformatics, University of Georgia, 120 Green Street, Athens, GA 30602, United States
- Department of Genetics, University of Georgia, 120 West Green Street, Athens, GA 30602, United States
| | - Liang Liu
- Institute of Bioinformatics, University of Georgia, 120 Green Street, Athens, GA 30602, United States
- Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA 30602, United States
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Susvitasari K, Tupper P, Stockdale JE, Colijn C. A method to estimate the serial interval distribution under partially-sampled data. Epidemics 2023; 45:100733. [PMID: 38056165 DOI: 10.1016/j.epidem.2023.100733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 11/22/2023] [Accepted: 11/26/2023] [Indexed: 12/08/2023] Open
Abstract
The serial interval of an infectious disease is an important variable in epidemiology. It is defined as the period of time between the symptom onset times of the infector and infectee in a direct transmission pair. Under partially sampled data, purported infector-infectee pairs may actually be separated by one or more unsampled cases in between. Misunderstanding such pairs as direct transmissions will result in overestimating the length of serial intervals. On the other hand, two cases that are infected by an unseen third case (known as coprimary transmission) may be classified as a direct transmission pair, leading to an underestimation of the serial interval. Here, we introduce a method to jointly estimate the distribution of serial intervals factoring in these two sources of error. We simultaneously estimate the distribution of the number of unsampled intermediate cases between purported infector-infectee pairs, as well as the fraction of such pairs that are coprimary. We also extend our method to situations where each infectee has multiple possible infectors, and show how to factor this additional source of uncertainty into our estimates. We assess our method's performance on simulated data sets and find that our method provides consistent and robust estimates. We also apply our method to data from real-life outbreaks of four infectious diseases and compare our results with published results. With similar accuracy, our method of estimating serial interval distribution provides unique advantages, allowing its application in settings of low sampling rates and large population sizes, such as widespread community transmission tracked by routine public health surveillance.
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Affiliation(s)
| | - Paul Tupper
- Department of Mathematics, Simon Fraser University, Canada
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Wang Z, Wu P, Wang L, Li B, Liu Y, Ge Y, Wang R, Wang L, Tan H, Wu CH, Laine M, Salje H, Song H. Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study. PLoS Comput Biol 2023; 19:e1011492. [PMID: 37721947 PMCID: PMC10538769 DOI: 10.1371/journal.pcbi.1011492] [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: 05/23/2023] [Revised: 09/28/2023] [Accepted: 09/05/2023] [Indexed: 09/20/2023] Open
Abstract
China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.
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Affiliation(s)
- Zengmiao Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Peiyi Wu
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Bingying Li
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Yonghong Liu
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Yuxi Ge
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Ruixue Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Ligui Wang
- Center of Disease Control and Prevention, PLA, Beijing, China
| | - Hua Tan
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, Southampton, United Kingdom
| | - Marko Laine
- Finnish Meteorological Institute, Meteorological Research Unit, Helsinki, Finland
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Hongbin Song
- Center of Disease Control and Prevention, PLA, Beijing, China
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