Reporting delays: a widely neglected impact factor in COVID-19 forecasts (preprint)

Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean and devoid of noise? Common sense implies the negative answer. While we cannot evaluate the cleanliness of the COVID-19 epidemic data in a holistic fashion, we can assess the data for the presence of reporting delays. In our work, through the analysis of the first COVID-19 wave, we find substantial reporting delays in the published epidemic data. Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series. Our framework can uncover and analyze reporting delays in 8 regions significantly affected by the first COVID-19 wave. Further, we demonstrate that removing reporting delays from epidemic data using our statistical framework may decrease the error in epidemic forecasts. While our statistical framework can be used in combination with any epidemic forecast method that intakes infectious, recovered, and deceased data, to make a basic assessment, we employed the classical SIRD epidemic model. Our results indicate that the removal of reporting delays from the epidemic data may decrease the forecast error by up to 50. We anticipate that our framework will be indispensable in the analysis of novel COVID-19 strains and other existing or novel infectious diseases.

Two-population SIR model and strategies to reduce mortality in pandemics (preprint)

Despite many studies on the transmission mechanism of the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it remains still challenging to efficiently reduce mortality. In this work, we apply a two-population Susceptible-Infected-Removed (SIR) model to investigate the COVID-19 spreading when contacts between elderly and non-elderly individuals are reduced due to the high mortality risk of elderly people. We discover that the reduction of connections between two populations can delay the death curve but cannot well reduce the final mortality. We propose a merged SIR model, which advises elderly individuals to interact less with their non-elderly connections at the initial stage but interact more with their non-elderly relationships later, to reduce the final mortality. Finally, immunizing elderly hub individuals can also significantly decrease mortality.

Network-based Prediction of COVID-19 Epidemic Spreading in Italy (preprint)

Initially emerged in the Chinese city Wuhan and subsequently spread almost worldwide causing a pandemic, the SARS-CoV-2 virus follows reasonably well the SIR (Susceptible-Infectious-Recovered) epidemic model on contact networks in the Chinese case. In this paper, we investigate the prediction accuracy of the SIR model on networks also for Italy. Specifically, the Italian regions are a metapopulation represented by network nodes and the network links are the interactions between those regions. Then, we modify the network-based SIR model in order to take into account the different lockdown measures adopted by the Italian Government in the various phases of the spreading of the COVID-19. Our results indicate that the network-based model better predicts the daily cumulative infected individuals when time-varying lockdown protocols are incorporated in the classical SIR model.

Mobile smartphone tracing can detect almost all SARS-CoV-2 infections (preprint)

Currently, many countries are considering the introduction of tracing software on mobile smartphones with the main purpose to inform and alarm the mobile app user. Here, we demonstrate that, in addition to alarming and informing, mobile tracing can detect nearly all users that are infected by SARS-CoV-2. Our algorithm BETIS (Bayesian Estimation for Tracing Infection States) makes use of self-reports of the user's health status. Then, BETIS guarantees that almost all SARS-CoV-2 infections of the group of users can be detected. Furthermore, BETIS estimates the virus prevalence in the whole population, consisting of users and non-users. BETIS is based on a hidden Markov epidemic model and recursive Bayesian filtering. The potential that mobile tracing apps, in addition to medical testing and quarantining, can eradicate COVID-19 may persuade citizens to trade-off privacy against public health.

The lighting of the BECONs: A behavioral data science approach to tracking interventions in COVID-19 research (preprint)

The imposition of lockdowns in response to the COVID-19 outbreak has underscored the importance of human behavior in mitigating virus transmission. Scientific study ofinterventions designed to change behavior (e.g., to promote social distancing) requiresmeasures of effectiveness that are fast, that can be assessed through experiments, and that can be investigated without actual virus transmission. This paper presents a methodological approach designed to deliver such indicators. We show that behavioral data, obtainable through tracing apps currently in development, can be used to assess a central concept in epidemiology known as the contact network: a network representation that encodes which individuals have been in physical proximity long enough to transmit the virus. Because behavioral interventions alter the contact network, a comparison of contact networks before and after the intervention can provide information on the effectiveness of the intervention. We coin indicators based on this idea Behavioral Contact Network (BECON) indicators. We examine the performance of three indicators: the Density BECON (based on differences in network density), the Spectral BECON (based on differences in the eigenvector of the adjacency matrix), and the ASPL BECON (based on differences in average shortest path lengths). Using simulations, we show that all three indicators can effectively track the effect of behavioral interventions. Even in conditions with significant amounts of noise, BECON indicators can reliably identify and order effect sizes of interventions. The present paper invites further study of the method as well as practical implementations to test the validity of BECON indicators in real data.