Spatiotemporal association between weather and Covid-19 explored by machine learning

The Covid-19 epidemic led to loss of the lives of many people in the world and had a very negative impact on the mental and physical health of humans. One of the effective ways to preventive strategies regarding is to study the impact of climatic parameters. This research introduces a new spatiotemporal methodology to explore the association between Covid-19 and hourly data of weather. This methodology developed based on machine learning using unsupervised clustering method. Six counties considered for finding association and the cities that have similar climatic temporal changes clustered and compared with cities that have similar number of Covid-19 cases. For this goal, a new model is developed for finding similarities between clusters, which indicates the association between weather and Covid-19. The result shows similarities are about 57% for wind speed, 63% for temperature, 63% for surface pressure, and 42% for elevation. Then result evaluated sing Kendall's tau_b and Spearman's rho which shows the proposed methodology has an acceptable result.

A spatiotemporal analysis of the impact of the COVID-19 outbreak on noise pollution in Tehran, Iran.

Noise pollution is one of the non-natural hazards in cities. Long-term exposure to this kind of pollution has severe destructive effects on human health, including mental illness, stress, anxiety, hormonal disorders, hypertension and therefore also cardiovascular disease. One of the primary sources of noise pollution in cities is transportation. The COVID-19 outbreak caused a significant change in the pattern of transportation in cities of Iran. In this article, we studied the spatial and temporal patterns of noise pollution levels in Tehran before and after the outbreak of this disease. An overall analysis from one year before until one year after the outbreak, which showed that noise pollution in residential areas of Tehran had increased by 7% over this period. In contrast, it had diminished by about 2% in the same period in the city centre and around Tehran's Grand Bazaar. Apart from these changes, we observed no specific pattern in other city areas. However, a monthly data analysis based on the t-test, the results show that the early months of the virus outbreak were associated with a significant pollution reduction. However, this reduction in noise pollution was not sustained; instead a gradual increase in pollution occurred over the following months. In the months towards the end of the period analysed, noise pollution increased to a level even higher than before the outbreak. This increase can be attributed to the gradual reopening of businesses or people ignoring the prevailing conditions.

Spatiotemporal Surveillance of COVID-19 Based on Epidemiological Features: Evidence from Northeast Iran

Spatiotemporal analysis of COVID-19 cases based on epidemiological characteristics leads to more refined findings about health inequalities and better allocation of medical resources in a spatially and timely fashion. While existing literature has explored the spatiotemporal clusters of COVID-19 worldwide, little attention has been paid to investigate the space-time clusters based on epidemiological features. This study aims to identify COVID-19 clusters by epidemiological factors in Golestan province, one of the highly affected areas in Iran. This cross-sectional study used GIS techniques, including local spatial autocorrelations, directional distribution statistics, and retrospective space-time Poisson scan statistics. The results demonstrated that Golestan has been facing an upward trend of epidemic waves, so the case fatality rate (CFR) of the province was roughly 2.5 times the CFR in Iran. Areas with a more proportion of young adults were more likely to generate space-time clusters. Most high-risk clusters have emerged since early June 2020. The infection first appeared in the west and southwest of the province and gradually spread to the center, east, and northeast regions. The results also indicated that the detected clusters based on epidemiological features varied across the province. This study provides an opportunity for health decision-makers to prioritize disease-prone areas and more vulnerable populations when allocating medical resources.

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

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.

An event-based model and a map visualization approach for spatiotemporal association relations discovery of diseases diffusion

Infectious disease diffusion is inherently a complex spatiotemporal phenomenon. Simplifying this complexity to discover the associated structure of the city is of great importance. However, existing approaches mainly focus on distance property in geographic space to examine randomness, dispersion, or clustered structure of the disease distribution. While, the outbreak continuously changes its properties, shapes, or locations. Regardless of this adjacency-based structure, there may be associated spatial units that exhibit similar behaviors towards the outbreak fluctuations in a city. To reveal these characteristics, this research proposes a novel event-based spatiotemporal model, mining associated areas in space and time simultaneously. This model was applied to the cases rate of COVID-19 at the ZIP Code level in New York City. The results showed that the proposed approach could sufficiently address the spatiotemporal association relationships. To better understand the discovered associations, a map visualization approach is introduced, allowing recognition of these association relations at a glance. This approach develops a deep understanding of the spatiotemporal structure of the outbreak and better manifests the association and cause-and-effect relations between ZIP Code areas. The results provide good assets for the construction of healthy resilient cities with the function of preventing epidemic crises in the future.