Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdowns
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Abstract
Background
COVID-19 is widely spreading across the globe right now. While some countries have flattened the curve, others are struggling to control the spread of the infection. Precise risk prediction modeling is key to accurate prevention and containment of COVID-19 infection, as well as for the preparation of resources needed to deal with the pandemic in different regions.
Methods
Given the vast differences in approaches and scenarios used by these models to predict future infection rates, in this study, we compared the accuracy among different models such as regression models, ARIMA model, multilayer perceptron, vector autoregression, susceptible exposed infected recovered (SEIR), susceptible infected recovered (SIR), recurrent neural networks (RNNs), long short term memory networks (LSTM) and exponential growth model in prediction of the total COVID-19 confirmed cases. We did so by comparing the predicted rates of these models with actual rates of COVID-19 in India during the nationwide lockdowns.
Results
Few of these models accurately predicted COVID-19 incidence and mortality rates in six weeks, though some provided close results. While advanced warning can help mitigate and prepare for an impending or ongoing epidemic, using poorly fitting models for prediction could lead to substantial adverse outcomes.
Implications
As the COVID-19 pandemic continues, accurate risk prediction is key to effective public health interventions. Caution should be taken when choosing different risk prediction models based on specific scenarios and needs. To improve risk prediction of infectious disease such as COVID-19 for policy guidance and recommendations on best practices, both internal (e.g., specific virus characteristics in transmission and mutation) and external factors (e.g., large-scale human behaviors such as school opening, parties, and breaks) should be considered and appropriately weighed.
Article activity feed
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SciScore for 10.1101/2020.08.03.20167254: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar …
SciScore for 10.1101/2020.08.03.20167254: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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