Fear, Access, and the Real-Time Estimation of Etiological Parameters for Outbreaks of Novel Pathogens

Abstract

28Early analysis of outbreaks of novel pathogens to evaluate their likely public health 29 impact depends on fitting predictive models to data gathered and updated in real-time. 30Both transmission rates and the critical 0 R threshold (i.e. the pathogen's 'reproductive 31 number') are inferred by finding the values that provide the best model fit to reported 32 case incidence. These models and inferred results are then the basic tools used for public 33 health planning: how many people expected to be infected, at what scales of time and 34 space, and whether potential intervention strategies impact disease transmission and 35spread. An underlying assumption, however, is that the ability to observe new cases is 36 either constant, or at least constant relative to diagnostic test availability. 37 We present a demonstration, discussion, and mathematical analysis of how this 38 assumption of predictable observability in disease incidence can drastically impact model 39 accuracy. We also demonstrate how to tailor estimations of these parameters to a few 40 examples of different types of shifting influences acting on detection, depending on the 41 likely sensitivity of surveillance systems to errors from sources such as clinical testing 42 rates and differences in healthcare-seeking behavior from the public over time. Finally, 43we discuss the implications of these corrections for both historical and current outbreaks. 44 45

The effectiveness of contact tracing in mitigating COVID-19 outbreak: A model-based analysis in the context of India

“…Restriction in travel, closure of the educational institute, preventive specifications in various workplaces during this period changes the standard social mixing patterns, which delayed the impact of COVID-19 and delayed the disease onset in uninfected zones of the country. Consequences of human behavioral activity in predictive epidemiological models gained much attention in recent years [26] , [27] , [28] , [29] . Alert of a disease outbreak fundamentally results in avoiding unnecessary contact at an individual level, which gives a behavioral immunity in population [8] .…”

Homophily in risk and behavior complicate understanding the COVID-19 epidemic curve

“…Beyond mixing changes due to the holidays or weather-related behavior, there are also likely ongoing gradual changes in local adoption rates of mask wearing or social distancing, especially as local case incidence created greater local awareness of potential disease severity than may have been believed before direct observable outcomes due to differences in national reporting and media consumption (14)(15)(16)(17). These regional differences in behavior lead to differences in community vulnerability, causing a potential feedback loop between behavior and local outbreak severity (18).…”