Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions.

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Abstract

Policymakers during COVID-19 operate in uncharted territory and must make tough decisions. Operational Research - the ubiquitous 'science of better' - plays a vital role in supporting this decision-making process. To that end, using data from the USA, India, UK, Germany, and Singapore up to mid-April 2020, we provide predictive analytics tools for forecasting and planning during a pandemic. We forecast COVID-19 growth rates with statistical, epidemiological, machine- and deep-learning models, and a new hybrid forecasting method based on nearest neighbors and clustering. We further model and forecast the excess demand for products and services during the pandemic using auxiliary data (google trends) and simulating governmental decisions (lockdown). Our empirical results can immediately help policymakers and planners make better decisions during the ongoing and future pandemics.

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Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Dmitry Ivanov (2020)

Highlights • Epidemic outbreaks are a special case of supply chain (SC) risks. • We articulate the specific features of epidemic outbreaks in SCs. • We demonstrate a simulation model for epidemic outbreak analysis. • We use an example of coronavirus COVID-19 outbreak.

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Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

Zifeng Yang, Zhiqi Zeng, Ke Wang … (2020)

Background The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.

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Food supply chains during the COVID‐19 pandemic

Jill Hobbs (2020)

Abstract This paper provides an early assessment of the implications of the COVID‐19 pandemic for food supply chains and supply chain resilience. The effects of demand‐side shocks on food supply chains are discussed, including consumer panic buying behaviors with respect to key items, and the sudden change in consumption patterns away from the food service sector to meals prepared and consumed at home. Potential supply‐side disruptions to food supply chains are assessed, including labor shortages, disruptions to transportation networks, and “thickening” of the Canada–U.S. border with respect to the movement of goods. Finally, the paper considers whether the COVID‐19 pandemic will have longer‐lasting effects on the nature of food supply chains, including the growth of the online grocery delivery sector, and the extent to which consumers will prioritize “local” food supply chains.