Estimation of Individual Probabilities of COVID-19 Infection, Hospitalization, and Death From A County-level Contact of Unknown infection Status

Abstract

Objective: Our objective is to demonstrate a method to estimate the probability of a laboratory confirmed COVID19 infection, hospitalization, and death arising from a contact with an individual of unknown infection status. Methods: We calculate the probability of a confirmed infection, hospitalization, and death resulting from a county-level person-contact using available data on current case incidence, secondary attack rates, infectious periods, asymptomatic infections, and ratios of confirmed infections to hospitalizations and fatalities. Results: Among US counties with populations greater than 500,000 people, during the week ending June 13,2020, the median estimate of the county level probability of a confirmed infection is 1 infection in 40,500 person contacts (Range: 10,100 to 586,000). For a 50 to 64 year-old individual, the median estimate of the county level probability of a hospitalization is 1 in 709,000 person contacts (Range: 177,000 to 10,200,000) and the median estimate of the county level probability of a fatality is 1 in 6,670,000 person contacts (Range 1,680,000 to 97,600.000). Conclusions and Relevance: Estimates of the individual probabilities of COVID19 infection, hospitalization and death vary widely but may not align with public risk perceptions. Systematically collected and publicly reported data on infection incidence by, for example, the setting of exposure, type of residence and occupation would allow more precise estimates of probabilities than possible with currently available public data. Calculation of secondary attack rates by setting and better measures of the prevalence of seropositivity would further improve those estimates.

Managing the risk of a COVID-19 outbreak from border arrivals

“…at work); or environmental (there has been no direct contact between the index and secondary case, and transmission is via a contaminated surface or airborne particles). These are weighted by the relative attack rates for each contact route, w route , based on the observation that within-household transmission accounts for 2-3 times more infections than outside-household transmission (22, 23), and the assumption of (18) that around 10% of all transmission is environmental. The model has no fixed household size as we are only interested in the very early stages before the outbreak is detected, where only one or two household members are typically infected.…”

Retail store customer flow and COVID-19 transmission

“…Furthermore, a fraction πn of the population are not susceptible because they have been exposed to the virus and developed immunity (24), and therefore a fraction 1 − πn of the population are susceptible. In our model, susceptible customers have a direct exposure risk c, where c is defined as the probability of COVID-19 transmission whenever a susceptible customer passes, or is passed by, an infectious customer (25). Note that the two customers may be traveling in the same direction (one-way direct exposure) or in opposite directions (two-way direct exposure) when they pass each other.…”

COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source COVID-19 Outbreak Simulation Model

“…There are currently two key approaches for calculating the estimated number of both hospitalizations and deaths due to COVID-19: 1) using historical statistical probabilities, each of which is unique to each age group in a population (Bhatia and Klausner, 2020; Bi et al ., 2020) and 2) using historical COVID-19 hospitalizations-to-cases and deaths-to-cases ratios (Kobayashi et al ., 2020). We choose to follow a modified version of the second approach as it does not require 1) clustering the population into age-groups and 2) calculating the risk of each individual using the given probability, which both affect the complexity of the model and the simulation time.…”

“…1) Clustering the population based on age-groups. This has potential different effects on, for example, population, environmental conditions, mitigation measures (Bhatia and Klausner, 2020; Bi et al ., 2020). 2) Considering vaccinated persons as another new category of persons in a population.…”

“…1) Clustering the population based on age-groups. This has potential different effects on, for example, population, environmental conditions, mitigation measures (Bhatia and Klausner, 2020;Bi et al, 2020 To evaluate the correlation between the temperature data and the number of daily confirmed COVID-19 cases or the daily counts of death, we use a generalized additive model (GAM). GAM is usually used to calculate the linear and non-linear regression models between meteorological factors (e.g., temperature, humidity) with COVID-19 infection and transmission [3,4,5].…”