Sub-epidemic Model Forecasts During the First Wave of the COVID-19 Pandemic in the USA and European Hotspots

Mathematical models have been widely used to understand the dynamics of the ongoing coronavirus disease 2019 (COVID-19) pandemic as well as to predict future trends and assess intervention strategies. The asynchronicity of infection patterns during this pandemic illustrates the need for models that can capture dynamics beyond a single-peak trajectory to forecast the worldwide spread and for the spread within nations and within other sub-regions at various geographic scales. Here, we demonstrate a five-parameter sub-epidemic wave modeling framework that provides a simple characterization of unfolding trajectories of COVID-19 epidemics that are progressing across the world at different spatial scales. We calibrate the model to daily reported COVID-19 incidence data to generate six sequential weekly forecasts for five European countries and five hotspot states within the United States. The sub-epidemic approach captures the rise to an initial peak followed by a wide range of post-peak behavior, ranging from a typical decline to a steady incidence level to repeated small waves for sub-epidemic outbreaks. We show that the sub-epidemic model outperforms a three-parameter Richards model, in terms of calibration and forecasting performance, and yields excellent short- and intermediate-term forecasts that are not attainable with other single-peak transmission models of similar complexity. Overall, this approach predicts that a relaxation of social distancing measures would result in continuing sub-epidemics and ongoing endemic transmission. We illustrate how this view of the epidemic could help data scientists and policymakers better understand and predict the underlying transmission dynamics of COVID-19, as early detection of potential sub-epidemics can inform model-based decisions for tighter distancing controls. © 2022, Springer Nature Switzerland AG.

Changes in testing rates could mask the novel Coronavirus disease (COVID-19) growth rate. (Coronavirus (COVID-19) collection.)

Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly spread around the world, leading to one of the most significant pandemic events of recent history. Deriving reliable estimates of the COVID-19 epidemic growth rate is quite important to guide the timing and intensity of intervention strategies. Indeed, many studies have quantified the epidemic growth rate using time-series of reported cases during the early phase of the outbreak to estimate the basic reproduction number, R0. Using daily time series of COVID-19 incidence, we illustrate how epidemic curves of reported cases may not always reflect the true epidemic growth rate due to changes in testing rates, which could be influenced by limited diagnostic testing capacity during the early epidemic phase.

USING SIMPLE DYNAMIC ANALYTIC FRAMEWORK TO CHARACTERIZE AND FORECAST EPIDEMICS

Mathematical modeling provides a powerful analytic framework to investigate the transmission and control of infectious diseases. However, the reliability of the results stemming from modeling studies heavily depend on the validity of assumptions underlying the models as well as the quality of data that is employed to calibrate them. When substantial uncertainty about the epidemiology of newly emerging diseases (e.g. the generation interval, asymptomatic transmission) hampers the application of mechanistic models that incorporate modes of transmission and parameters characterizing the natural history of the disease, phenomenological growth models provide a starting point to make inferences about key transmission parameters, such as the reproduction number, and forecast the trajectory of the epidemic in order to inform public health policies. We describe in detail the methodology and application of three phenomenological growth models, the generalized-growth model, generalized logistic growth model and the Richards model in context of the COVID-19 epidemic in Pakistan.

Spatial variability in the risk of death from COVID-19 in Italy.

OBJECTIVES: Italy has been badly affected by the COVID-19 pandemic and has one of the highest death tolls. We analyzed the severity of COVID-19 across all 20 Italian regions.METHOD: We manually retrieved the daily cumulative numbers of laboratory-confirmed cases and deaths attributed to COVID-19 in each region, and estimated the crude case fatality ratio and time delay-adjusted case fatality ratio (aCFR). We then assessed the association between aCFR and sociodemographic, health care and transmission factors using multivariate regression analysis.RESULTS: The overall aCFR in Italy was estimated at 17.4%. Lombardia exhibited the highest aCFR (24.7%), followed by Marche (19.3%), Emilia Romagna (17.7%) and Liguria (17.6%). Our aCFR estimate was greater than 10% for 12 regions. Our aCFR estimates were statistically associated with population density and cumulative morbidity rate in a multivariate analysis.CONCLUSION: Our aCFR estimates for Italy as a whole and for seven out of the 20 regions exceeded those reported for the most badly affected region in China. These findings highlight the importance of social distancing to suppress transmission to avoid overwhelming the health care system and reduce the risk of death.

Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020.

The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The Chinese government has implemented containment strategies of city-wide lockdowns, screening at airports and train stations, and isolation of suspected patients; however, the cumulative case count keeps growing every day. The ongoing outbreak presents a challenge for modelers, as limited data are available on the early growth trajectory, and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated. We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China. Here, we provide 5, 10, and 15 day forecasts for five consecutive days, February 5th through February 9th, with quantified uncertainty based on a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model. Our most recent forecasts reported here, based on data up until February 9, 2020, largely agree across the three models presented and suggest an average range of 7409-7496 additional confirmed cases in Hubei and 1128-1929 additional cases in other provinces within the next five days. Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588-13,499 in other provinces by February 24, 2020. Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates (February 7th - 9th). We also observe that each of the models predicts that the epidemic has reached saturation in both Hubei and other provinces. Our findings suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days.