Grant Number:	4U01CA261277-02 Interpret this number
Primary Investigator:	Lipsitch, Marc
Organization:	Harvard University D/B/A Harvard School Of Public Health
Project Title:	Casual, Statistical and Mathematical Modeling with Serologic Data
Fiscal Year:	2023

We will develop methods to enhance the design and analysis of serologic studies of populations with respect to COVID-19, including methods that may be generalized in the future to address challenges raised by other seasonal diseases (such as influenza) and newly emerging diseases. In addition, we will use serologic data in innovative ways to underpin mathematical models that can project population-level trends. Early serosurveys using convenience samples of the population and serologic assays with variable and often uncertain sensitivity and specificity were heavily criticized, for unrepresentativeness and inadequate accounting for test characteristics, resulting in bias and overconfidence (unduly narrow confidence bounds). Aim 1 will develop methods for valid inference of seroprevalence, specifically by (a) accounting for biased sampling, (b) accounting for imperfect tests, and (c) developing and testing a novel approach to snowball sampling employing serologic tests to enhance outbreak detection and contact tracing. Valid comparisons that assess seroprotection—whether, how much, and how long an individual is protected by an immune response to a COVID-19 infection (specifically, by antibodies) against reinfection—rely on adequate control for confounding, an issue that arises in multiple ways specific to seroprotection studies. Likewise, waning of seroprotection may be inferred in error if studies are not carefully designed and analyzed. The unprecedented efforts to develop detailed serologic and systems serologic data sets provide new forms of data that can be leveraged to better inform these inferences. Aim 2 will develop a suite of methods to enhance causal inference in seroprotection studies, including (a) sample size and power calculations; and (b) improved exploitation of serological data to reduce biases due to confounding and risk compensation. Aim 3 will develop new mathematical modeling approaches and apply them to quantify the likely reduction in the herd immunity threshold for COVID-19 due to various forms of risk heterogeneity and assortativeness in mixing. Aim 4 will develop models of COVID-19 transmission that accommodate emerging evidence about the duration and nature of immunity to infection, shedding, and symptoms, to obtain estimates of how illness attack rates will differ under varying assumptions about the progress of immunity. Aim 5 will develop transmission models to assess optimal cohorting arrangements in congregate facilities (eg prisons and nursing homes), with special attention to the nature of immunity required for these arrangements to be beneficial. Finally, vaccine supplies may be initially limited, necessitating efficient use of them. Aim 6 will investigate the use of serologic data in combination with other types of data to optimize allocation of scarce vaccines.




In the time of COVID-19: challenges, successes, and lessons learned from studies in cancer patients.
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Source: Journal Of The National Cancer Institute, 2025-08-01 00:00:00.0; 117(8), p. 1547-1556.
PMID: 40127178
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Authors: Deichmann J. , Barda N. , Canetti M. , Peretz Y. , Weiss-Ottolenghi Y. , Lustig Y. , Regev-Yochay G. , Lipsitch M. .
Source: Plos Computational Biology, 2025 Jun; 21(6), p. e1013192.
EPub date: 2025-06-18 00:00:00.0.
PMID: 40531962
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The importance of playing the long game when it comes to pandemic surveillance.
Authors: Shearer F.M. , Lipsitch M. .
Source: Proceedings Of The National Academy Of Sciences Of The United States Of America, 2025-04-15 00:00:00.0; 122(15), p. e2500328122.
EPub date: 2025-04-09 00:00:00.0.
PMID: 40203044
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Seasonal forcing and waning immunity drive the sub-annual periodicity of the COVID-19 epidemic.
Authors: Rubin I.N. , Bushman M. , Lipsitch M. , Hanage W.P. .
Source: Medrxiv : The Preprint Server For Health Sciences, 2025-03-10 00:00:00.0; , .
EPub date: 2025-03-10 00:00:00.0.
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Authors: Olejarz J. , Hoffmann T. , Zapf A. , Mugahid D. , Molinaro R. , Brown C. , Boltyenkov A. , Dudykevych T. , Gupta A. , Lipsitch M. , et al. .
Source: Medrxiv : The Preprint Server For Health Sciences, 2025-02-05 00:00:00.0; , .
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Causal Estimands for Analyses of Averted and Avertible Outcomes due to Infectious Disease Interventions.
Authors: Jia K.M. , Boyer C.B. , Wallinga J. , Lipsitch M. .
Source: Epidemiology (cambridge, Mass.), 2025-01-24 00:00:00.0; 36(3), p. 363-73.
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Considerations for viral co-infection studies in human populations.
Authors: Chin T. , Foxman E.F. , Watkins T.A. , Lipsitch M. .
Source: Mbio, 2024-07-17 00:00:00.0; 15(7), p. e0065824.
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Estimated excess deaths due to COVID-19 among the urban population of Mainland China, December 2022 to January 2023.
Authors: Raphson L. , Lipsitch M. .
Source: Epidemiology (cambridge, Mass.), 2024-01-29 00:00:00.0; , .
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Authors: Kahn R. , Feikin D.R. , Wiegand R.E. , Lipsitch M. .
Source: American Journal Of Epidemiology, 2024-01-08 00:00:00.0; 193(1), p. 232-234.
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Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky.
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Source: Environmental Research, 2024-01-01 00:00:00.0; 240(Pt 2), p. 117395.
EPub date: 2023-10-12 00:00:00.0.
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Some principles for using epidemiologic study results to parameterize transmission models.
Authors: Joshi K. , Kahn R. , Boyer C. , Lipsitch M. .
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Source: Medrxiv : The Preprint Server For Health Sciences, 2023-09-25 00:00:00.0; , .
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Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County.
Authors: Jia K.M. , Kahn R. , Fisher R. , Balter S. , Lipsitch M. .
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Estimated preventable COVID-19-associated deaths due to non-vaccination in the United States.
Authors: Jia K.M. , Hanage W.P. , Lipsitch M. , Johnson A.G. , Amin A.B. , Ali A.R. , Scobie H.M. , Swerdlow D.L. .
Source: European Journal Of Epidemiology, 2023-04-24 00:00:00.0; , p. 1-4.
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Comparative performance of between-population vaccine allocation strategies with applications for emerging pandemics.
Authors: Joshi K. , Rumpler E. , Kennedy-Shaffer L. , Bosan R. , Lipsitch M. .
Source: Vaccine, 2023-03-10 00:00:00.0; 41(11), p. 1864-1874.
EPub date: 2023-01-23 00:00:00.0.
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Geographically skewed recruitment and COVID-19 seroprevalence estimates: a cross-sectional serosurveillance study and mathematical modelling analysis.
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Source: Bmj Open, 2023-03-07 00:00:00.0; 13(3), p. e061840.
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Fairness and efficiency considerations in COVID-19 vaccine allocation strategies: A case study comparing front-line workers and 65-74 year olds in the United States.
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Source: Plos Global Public Health, 2023; 3(2), p. e0001378.
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Optimizing prevalence estimates for a novel pathogen by reducing uncertainty in test characteristics.
Authors: Larremore D.B. , Fosdick B.K. , Zhang S. , Grad Y.H. .
Source: Epidemics, 2022 Dec; 41, p. 100634.
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Infectious disease dynamics and restrictions on social gathering size.
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Source: American Journal Of Epidemiology, 2022-03-24 00:00:00.0; 191(5), p. 800-811.
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SARS-CoV-2 breakthrough infections in vaccinated individuals: measurement, causes and impact.
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Estimating epidemiologic dynamics from cross-sectional viral load distributions.
Authors: Hay J.A. , Kennedy-Shaffer L. , Kanjilal S. , Lennon N.J. , Gabriel S.B. , Lipsitch M. , Mina M.J. .
Source: Science (new York, N.y.), 2021-07-16 00:00:00.0; 373(6552), .
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EPub date: 2021-06-21 00:00:00.0.
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Interpreting vaccine efficacy trial results for infection and transmission.
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EPub date: 2021-04-01 00:00:00.0.
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Mathematical modeling to inform vaccination strategies and testing approaches for COVID-19 in nursing homes.
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Interpreting vaccine efficacy trial results for infection and transmission.
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Model-informed COVID-19 vaccine prioritization strategies by age and serostatus.
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