A Review of Epidemic Forecasting Using Artificial Neural Networks

Abstract

Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. As much as forecast precision is important, methods and model selection procedures are critical to forecast precision. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is necessary for forecasters to apply the right tools for the epidemic forecasts with high precision. Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with other classical methods or meta-heuristics that improved performance of epidemic forecasts were analysed. Results: Implementing hybrid ANN using data transformation techniques based on improved algorithms, combining forecast models, and using technological platforms enhance the learning and generalization of ANN in forecasting epidemics. Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models, data transformation and technology should be used for an epidemic forecast.

COVID-19 in Iran: A Deeper Look Into The Future

“…Finally the models' performances are measure using mean average percentage error (MAPE) metric. We experimented with more than 8 machine learning models, but to be concise, only report the result of four promising ones, which includes, Random Forest (RF) [23], multi-layer perceptron (MLP) [24], Long short-term memory (LSTM) [25] For each of these models, different structures (hyper-parameters and parameters) are examined and best performing architectures are summarized in Table 1…”

Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon

“…That said, artificial neural network (ANN) is a technique that can be used to model epidemiological phenomena, forecast epidemic peaks, and estimate the dimension of the risk and scope of diseases [ 18 – 21 ]. The main characteristic of ANN is self-learning without prior knowledge of the complex non-linear relationships that exist between the input and output variables [ 22 ].…”

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran

“…This study investigated the characteristics, symptoms and underlying diseases of COVID-19 patients in 6 provinces of Iran and compared them to know if these cases are significantly different. Although the epidemic prediction is essential for applying effective prevention and control of infectious diseases [ 7 ], it has been somewhat neglected in research for COVID-19 by now. Hence, using data obtained from hospitalized suspected COVID-19 patients, the ANN and LR models were developed for diagnostics of COVID-19-infected and not-infected patients.…”

“…Considering the highly contagious nature and high prevalence of COVID-19, model development for the diagnosis of COVID-19 is considered to be a crucial measure for the control of the disease. Many studies have applied the multilayer perceptron neural network and logistic regression in the diagnosis of infectious disease [ 7 ]. But no studies have compared the abilities of ANN and LR models to predict the COVID-19 infection.…”

“…In the current situation, early diagnosis of coronavirus infection and timely treatment reduces its complications and spread [ 4 ]. Until now, artificial intelligence and logistical regression have been used to diagnose various diseases in many studies [ [5] , [6] , [7] ].…”