A multi-type branching process model for epidemics with application to COVID-19

Abstract

In this paper we model an infectious disease epidemic using Multi-type Branching Process where the number of offsprings of different types follow non-identical Poisson distributions whose parameters may vary over time. We allow for variation in parameters due to the behavior of citizens, government interventions in the form of lockdown, testing and contact tracing and the infectiousness of the variant of the virus in circulation at a time-point in a location. The model can be used to estimate several unknown quantities of interest in an epidemic such as the number of undetected cases and number of people quarantined following contact tracing. The model is fitted to the publicly available COVID-19 caseload data of India, South Korea, UK and US and is seen to provide good fit. It also provides good short-term forecast of the caseload for these countries. This model can be useful for health policy planners in assessing the impact of various intervention strategies such as testing, contact tracing, quarantine etc.

Attitudes towards booster, testing and isolation, and their impact on COVID-19 response in winter 2022/2023 in France, Belgium, and Italy

“…First, the model is not age-stratified. This is due to the branching process approximation, typically used to study the effects of testing, tracing and isolation in detail [30][31][32][33][34] . However, we do consider the age-specific response to booster uptake in the multi-country survey to infer the population-level vaccine-induced immunity for the 2022/2023 winter.…”

Limiting Behaviors of Stochastic Spread Models Using Branching Processes

“…Laha [16] considered a time-varying multitype branching process model for the spread of infectious disease in which it is assumed that all detected individuals are completely contact traced and the expected numbers of detected infected individuals, undetected infected individuals, and the total number of cases up to generation n are computed. Laha and Majumdar [17] extended the multitype branching model to the case in which the contact tracing is only partial and used Poisson offspring distributions whose parameters may vary over time to investigate the expected number of individuals in each category. Yanev et al [18] considered both two-type (infected undetected vs infected detected) branching processes with or without immigration and studied the growth rate and so on.…”