Factors associated with COVID-19 and predictive modelling of spread across five urban metropolises in the world

ABSTRACT

The novel coronavirus SARS CoV-2 (COVID-19) is a pandemic disease and became a public health emergency worldwide. In the present study, comparison among most vigorously affected select cities across different countries, Delhi (India), Madrid (Spain), Lombardy (Italy), and New York and New Jersey (USA), were carried out up to July 2020. Predictive modelling was employed by using machine learning algorithms to predict the spread of the virus during August to November 2020. The results indicated that population density and urban density have a stronger connection with the spread of COVID-19 across the cities of Madrid, New York, and New Jersey. Conversely, Milan has exhibited a higher infection rate despite low population density (420 persons/km2) because of delayed human-human transmission measures and lockdowns. Relatively lower infection was recorded in Delhi even with higher population density (11,312 persons/km2) and higher urban compactness, which can be attributed to timely lockdowns and social distancing measures. The temperature and humidity have also abetted the spread of virus, especially in temperate regions with threshold temperature <10 °C with a humidity level between 60 and 77 g/m3. Predictive modelling reveals withdrawal of the pandemic across select cities by the end of 2020. The above findings would assist policymakers in making appropriate decisions for preventing the spread of this novel virus. © Springer Nature Switzerland AG 2021.