Abstract
The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic–organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces.
Highlights
The first “Coronavirus Disease 2019” (COVID-19) cases were discovered from an initial cluster of pneumonia of unknown etiology in the metropolitan city of Wuhan, province of Hubei, mainland China, in late December 2019 [1]
The following section will provide a brief overview of the various indicators that were used as features for the Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) model
Using the appropriate threshold discovered for each province in South Africa, shown in Table 4, the alert system was tested by comparing the system predicted start date of the second wave against the actual case data
Summary
The first “Coronavirus Disease 2019” (COVID-19) cases were discovered from an initial cluster of pneumonia of unknown etiology in the metropolitan city of Wuhan, province of Hubei, mainland China, in late December 2019 [1] It is caused by an infectious agent known as “Severe Acute Respiratory Syndrome-related Coronavirus type 2” (SARS-CoV-2), the contraction of which results in a generally mild or even asymptomatic infection, that can, in a fraction of patients, evolve into a serious, life-threatening communicable disease [2]. The nature of the way the virus spreads causes cases to come in further recurring waves This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen, for which there was no community cross-protective immunity, with the population being substantially naive to the virus [4]. Other determinants of the epidemic trends of the COVID-19 pandemic include seasonal factors [8], and meteorological drivers [9], as well as community heterogeneity and complex, highly heterogeneous social networks, with phenomena such as over-dispersion, super-spreading events, super-spreaders [10]
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