Spatial Modeling of COVID-19 Prevalence Using Adaptive Neuro-Fuzzy Inference System

Abstract

This study is dedicated to modeling the spatial variation in COVID-19 prevalence using the adaptive neuro-fuzzy inference system (ANFIS) when dealing with nonlinear relationships, especially useful for small areas or small sample size problems. We compiled a broad range of socio-demographic, environmental, and climatic factors along with potentially related urban land uses to predict COVID-19 prevalence in rural districts of the Golestan province northeast of Iran with a very high-case fatality ratio (9.06%) during the first year of the pandemic (2020–2021). We also compared the ANFIS and principal component analysis (PCA)-ANFIS methods for modeling COVID-19 prevalence in a geographical information system framework. Our results showed that combined with the PCA, the ANFIS accuracy significantly increased. The PCA-ANFIS model showed a superior performance (R2 (determination coefficient) = 0.615, MAE (mean absolute error) = 0.104, MSE (mean square error) = 0.020, and RMSE (root mean square error) = 0.139) than the ANFIS model (R2 = 0.543, MAE = 0.137, MSE = 0.034, and RMSE = 0.185). The sensitivity analysis of the ANFIS model indicated that migration rate, employment rate, the number of days with rainfall, and residential apartment units were the most contributing factors in predicting COVID-19 prevalence in the Golestan province. Our findings indicated the ability of the ANFIS model in dealing with nonlinear parameters, particularly for small sample sizes. Identifying the main factors in the spread of COVID-19 may provide useful insights for health policymakers to effectively mitigate the high prevalence of the disease.

Spatio-Temporal Modeling of COVID-19 Spread in Relation to Urban Land Uses: An Agent-Based Approach

“…The spread of COVID-19 was found to be more concentrated in the central areas (Figure 10A), possibly due to the high population density and urban land use, which increase the likelihood of human contact and exposure to the virus. These findings are in line with the studies of Tabasi et al [23], Xu et al [42], and Razavi-Termeh et al [43], who illustrated those areas with high population density, better economic status, higher socio-economic complexity, and a higher employment rate tend to have greater impacts on the spread of the disease. From a practical standpoint, urban managers can prevent the excessive spread of the disease in areas with high-density urban land use by designing urban land use in a decentralized manner in the urban environment.…”

“…They only simulated human agents in four age groups: 0-5, 5-24, 24-64, and 64 years and older, while cases and deaths in other age groups are also common [5]. In addition, only limited types of urban land use, including schools, offices, and business areas, were included, while other land use types, such as health centers, universities, and recreational, cultural, and sports centers, can also be influential in the spread of the disease [23]. They assumed limited states of the epidemic situation (SEIRD) for a human agent.…”

“…With a population of about 350,000 people, Gorgan is the most populous city in the province and consists of 52 neighborhoods and 1911 blocks [22]. Densely populated communities and high-density urban land use can increase the likelihood of COVID-19 transmission and make it an ideal location to study the spatial dynamics of the disease and evaluate the effectiveness of various intervention strategies [23].…”