COVIDHunter: COVID-19 Pandemic Wave Prediction and Mitigation via Seasonality Aware Modeling

Abstract

Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region, predicts COVID-19 statistics (the daily number of cases, hospitalizations, and deaths), and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity), different variants of concern, vaccination rate, and mitigation measures. Using Switzerland as a case study, COVIDHunter estimates that we are experiencing a deadly new wave that will peak on 26 January 2022, which is very similar in numbers to the wave we had in February 2020. The policy-makers have only one choice that is to increase the strength of the currently applied mitigation measures for 30 days. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures. We release the source code of the COVIDHunter implementation at https://github.com/CMU-SAFARI/COVIDHunter and show how to flexibly configure our model for any scenario and easily extend it for different measures and conditions than we account for.

COWAVE: A labelled COVID-19 wave dataset for building predictive models

“…Yet, the most common approach has been modelling the disease using a susceptible-infected-removed (SIR) model or its variants [10]. Various methods of computing the parameters involved in the differential equations of populations, lead to different predictions of waves [11][12][13]. Some models have used Bayesian Learning to estimate these parameters [14,15].…”

GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping

“…Long read genome sequencing technologies [1][2][3][4] have significantly advanced the development of several genomic fields, such as personalized medicine [5][6][7][8][9][10][11], forensic science [12,13], evolutionary biology [14][15][16][17][18][19], and investigation of infectious disease outbreaks, especially during the COVID-19 pandemic [20][21][22][23][24][25][26][27][28][29][30]. Oxford Nanopore Technology (ONT) [4] is one of the most widely-used long-read sequencing technologies.…”