Abstract
We present a forecasting model aim to predict hospital occupancy in metropolitan areas during the current COVID-19 pandemic. Our SEIRD type model features asymptomatic and symptomatic infections with detailed hospital dynamics. We model explicitly branching probabilities and non-exponential residence times in each latent and infected compartments. Using both hospital admittance confirmed cases and deaths, we infer the contact rate and the initial conditions of the dynamical system, considering breakpoints to model lockdown interventions and the increase in effective population size due to lockdown relaxation. The latter features let us model lockdown-induced 2nd waves. Our Bayesian approach allows us to produce timely probabilistic forecasts of hospital demand. We have applied the model to analyze more than 70 metropolitan areas and 32 states in Mexico.
Highlights
The ongoing COVID-19 pandemic has posed a major challenge to public health systems in many countries with the imminent risk of saturated hospitals and patients not receiving proper medical care
In terms of disease handling, two leading issues determining the current situation are the lack of pharmaceutical treatment and our inability to estimate the extent of the asymptomatic infection of COVID-19 [5,6,7]
The model has two kinds of parameters that have to be calibrated or inferred; the ones related to COVID-19 disease and hospitalization dynamics and those associated with the public response to mitigation measures such as the contact rates β’s and the effective population size Neff during the outbreak
Summary
The ongoing COVID-19 pandemic has posed a major challenge to public health systems in many countries with the imminent risk of saturated hospitals and patients not receiving proper medical care. Control measures reduce new infections by limiting the number of contacts through mitigation and suppression [1]. Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic populations’ capacity to comply with control measures. We use Bayesian inference to estimate the initial state of the governing equations, the contact rate, and a proxy of the population size to make probabilistic forecasts of the required hospital beds, including the number of intensive care units. Assuming a given fraction of asymptomatic individuals, we infer changes in the transmission rate and the effective population size before and after a given lockdown–relaxation day. Inferred changes in effective population size allows us to produce a forecast of lockdowninduced 2nd waves. Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic. The model does not account explicitly for biases due to behavioral changes [20, 21], population clustering and super spreading events [22], we argue that our approach to lockdowns and relaxation events is a proxy model of these more general events
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