Infectious Diseases

National governments worldwide have implemented non-pharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects.

Objective: The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth—the percentage change in total cumulative cases—across 14 days for 114 countries using non-pharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national socio-cultural norms.

Methods: We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non–time series machine learning models in predicting confirmed infection growth.

We used three validation methods—in-distribution, out-of-distribution, and country-based cross-validation—for the evaluation, each of which was applicable to a different use case of the models.

Results: Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression.

Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case.

Conclusions:  This study adds to the rapidly growing body of work related to predicting COVID-19 infection rates by introducing an approach that incorporates routinely available data on NPIs and cultural dimensions. Importantly, this study emphasizes the utility of NPIs and cultural dimensions for predicting country-level growth of confirmed infections of COVID-19, which to date have been limited in existing forecasting models. Our findings offer a new direction for the broader inclusion of these types of measures, which are also relevant for other infectious diseases, using non–time series machine learning models. Our experiments also provide insight into validation methods for different applications of the models.

read the entire study at https://www.jmir.org/2021/4/e26628

The spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020.

The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations.

Objective: This paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health.

Methods: Considering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection.

Conclusions: In line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures.

read the study at https://medinform.jmir.org/2021/4/e24192