Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?

Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

The long-term dynamic relationship between communicable disease spread, economic prosperity, greenhouse gas emissions, and government health expenditures: preparing for COVID-19-like pandemics

The spread of communicable diseases, such as COVID-19, has a detrimental effect on our socio-economic structure. In a dynamic log-run world, socio-economic and environmental factors interact to spread communicable diseases. We investigated the long-term interdependence of communicable disease spread, economic prosperity, greenhouse gas emissions, and government health expenditures in India's densely populated economy using a variance error correction (VEC) approach. The VEC model was validated using stationarity, cointegration, autocorrelation, heteroscedasticity, and normality tests. Our impulse response and variance decomposition analyses revealed that economic prosperity (GNI) significantly impacts the spread of communicable diseases, greenhouse gas emissions, government health expenditures, and GNI. Current health expenditures can reduce the need for future increases, and the spread of communicable diseases is detrimental to economic growth. Developing economies should prioritize economic growth and health spending to combat pandemics. Simultaneously, the adverse effects of economic prosperity on environmental degradation should be mitigated through policy incentives.

Does climate help modeling COVID-19 risk and to what extent?

A growing number of studies suggest that climate may impact the spread of COVID-19. This hypothesis is supported by data from similar viral contagions, such as SARS and the 1918 Flu Pandemic, and corroborated by US influenza data. However, the extent to which climate may affect COVID-19 transmission rates and help modeling COVID-19 risk is still not well understood. This study demonstrates that such an understanding is attainable through the development of regression models that verify how climate contributes to modeling COVID-19 transmission, and the use of feature importance techniques that assess the relative weight of meteorological variables compared to epidemiological, socioeconomic, environmental, and global health factors. The ensuing results show that meteorological factors play a key role in regression models of COVID-19 risk, with ultraviolet radiation (UV) as the main driver. These results are corroborated by statistical correlation analyses and a panel data fixed-effect model confirming that UV radiation coefficients are significantly negatively correlated with COVID-19 transmission rates.

Modelling the persistence of Covid-19 positivity rate in Italy

The current Covid-19 pandemic is severely affecting public health and global economies. In this context, accurately predicting its evolution is essential for planning and providing resources effectively. This paper aims at capturing the dynamics of the positivity rate (PPR) of the novel coronavirus using the Heterogeneous Autoregressive (HAR) model. The use of this model is motivated by two main empirical features arising from the analysis of PPR time series: the changing long-run level and the persistent autocorrelation structure. Compared to the most frequently used Autoregressive Integrated Moving Average (ARIMA) models, the HAR is able to reproduce the strong persistence of the data by using components aggregated at different interval sizes, remaining parsimonious and easy to estimate. The relative merits of the proposed approach are assessed by performing a forecasting study on the Italian dataset. As a robustness check, the analysis of the positivity rate is also conducted by considering the case of the United States. The ability of the HAR-type models to predict the PPR at different horizons is evaluated through several loss functions, comparing the results with those generated by ARIMA models. The Model Confidence Set is used to test the significance of differences in the predictive performances of the models under analysis. Our findings suggest that HAR-type models significantly outperform ARIMA specifications in terms of forecasting accuracy. We also find that the PPR could represent an important metric for monitoring the evolution of hospitalizations, as the peak of patients in intensive care units occurs within 12-16 days after the peak in the positivity rate. This can help governments in planning socio-economic and health policies in advance.

Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic

From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.

Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5, and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values.

Modeling economic losses and greenhouse gas emissions reduction during the COVID-19 pandemic: Past, present, and future scenarios for Italy

Unprecedented nationwide lockdowns were adopted because of the COVID-19 pandemic. Understanding the socioeconomic impact of the past and future restrictions while assessing the resilience of a local economy emerged as a worldwide necessity. To predict the economic and environmental effects of the lockdowns, we propose a methodology based on the well-established input-output inoperability model, using Italy as a case study. By reconstructing the 2020 restrictions, we analyzed the economic losses and greenhouse gas emissions reductions, identifying the most economically impacted sectors because of the restrictions and the sectoral interdependencies and those avoiding most air emissions. We constructed four partial-lockdown scenarios by minimizing the economic losses for increasing restrictions to highlight the model's utility as a tool for policymaking. By revealing the most interconnected and, thus, crucial sectors, the simulated scenarios showcase how the restrictions can be selected to avoid sudden and unpredicted economic damage.

From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy

Italy was among the first countries to introduce drastic measures to reduce individual mobility in order to slow the diffusion of COVID-19. The first measures imposed by the central authorities on March 8, 2020, were unanticipated and highly localized, focusing on 26 provinces. Additional nationwide measures were imposed after one day, and were removed only after June 3. Looking at these watershed moments of the pandemic, this paper explores the impact of the adoption of localized restrictions on changes in individual mobility in Italy using a spatial discontinuity approach. Results show that these measures lowered individual mobility by 7 percentage points on top of the reduction in mobility recorded in the adjacent untreated areas. The study also fills a gap in the literature in that it looks at the changes in mobility after the nationwide restrictions were lifted and shows how the recovery in mobility patterns is related to various characteristics of local labour markets. Areas with a higher proportion of professions exposed to diseases, more suitable for flexible work arrangements, and with a higher share of fixed-term contracts before the pandemic are characterised by a smaller increase in mobility after re-opening.

International trade as critical parameter of COVID-19 spread that outclasses demographic, economic, environmental, and pollution factors

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the Coronavirus Disease 2019 (COVID-19), generating high numbers of COVID-19 related infected individuals and deaths, is still circulating in 2021 with new variants of the coronavirus, such that the state of emergency remains in manifold countries. Currently, there is still a lack of a full understanding of the factors determining the COVID-19 diffusion that clarify the causes of the variability of infections across different provinces and regions within countries. The main goal of this study is to explain new and main determinants underlying the diffusion of COVID-19 in society. This study focuses on international trade because this factor, in a globalized world, can synthetize different drivers of virus spread, such as mobility patterns, economic potentialities, and social interactions of an investigated areas. A case study research is performed on 107 provinces of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. Statistical analyses from March 2020 to February 2021 suggest that total import and export of provinces has a high association with confirmed cases over time (average r > 0.78, p-value <.001). Overall, then, this study suggests total import and export as complex indicator of COVID-19 transmission dynamics that outclasses other common parameters used to justify the COVID-19 spread, given by economic, demographic, environmental, and climate factors. In addition, this study proposes, for the first time, a time-dependent correlation analysis between trade data and COVID-19 infection cases to explain the relation between confirmed cases and social interactions that are a main source of the diffusion of SARS-CoV-2 and subsequent negative impact in society. These novel findings have main theoretical and practical implications directed to include a new parameter in modelling of the diffusion of COVID-19 pandemic to support effective policy responses of crisis management directed to constrain the impact of COVID-19 pandemic and similar infectious diseases in society.

Mobility in times of pandemics: Evidence on the spread of COVID19 in Italy's labour market areas

We investigate the interplay between the local spread of COVID-19 and patterns of individual mobility within and across self-contained geographical areas. Conceptually, we connect the debate on regional development in the presence of shocks with the literature on spatial labour markets and address some research questions about the role of individual mobility in affecting the spread of the disease. By looking at granular flows of Facebook users moving within and across Italian labour market areas (LMAs), we analyse whether their heterogeneous internal and external mobility has had a significant impact on excess mortality. We also explore how individual mobility plays different roles in LMAs hosting industrial districts - characterised by a thicker local labour market and denser business and social interactions - and with a high presence of "essential sectors" - activities not affected by the COVID-19 containment measures taken by the Italian government at the onset of the crisis.

Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis

COVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran’s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China’s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission.