Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic

Abstract

We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.

A Municipality-Based Approach Using Commuting Census Data to Characterize the Vulnerability to Influenza-Like Epidemic: The COVID-19 Application in Italy

“…In Italy, the currently available data on commuting are census data, with the advantage of being structured, open-source and representative of the entire Italian population. It is updated every 10 years does not seem to affect the spatial patterns of human mobility [7,12], thanks also to the stability of the production systems and persistence of attractive poles (schools, offices, etc.) in the same places.…”

“…On a local scale, other types of movement must be considered. Open-data resources and data-driven models offer many opportunities to improve governments' responses to the new epidemic and various studies have recently been conducted to assess how different human mobility data, such as Google's mobility data or data collected via mobile phone, can guide government and public health authorities to evaluate the effectiveness of measures to control the COVID-19 spread [7][8][9]. However, commuting, defined as the daily movements from residence to work or school, is certainly the most relevant and widely studied factor to describe spatial mobility in local models [10][11][12].…”

The Data Science of COVID-19 Spread: Some Troubling Current and Future Trends

“…These bureaucracies are concerned primarily with releasing legally required contemporary measurements and not maintaining consistent historical time series. This has resulted in the world's largest, most desperate scavenger hunt to scrape, transcribe, and translate counts disseminated in oral briefings, public websites, PDFs, and even static images (Alamo et al 2020). Teams from every country are working in often uncoordinated and duplicated efforts to compile government reporting into consistent panel data; these teams include newspapers (Sun et al 2020), nonprofits (USAFacts 2020), large private companies (Wolf, Ary, and Firooz 2020; Zhang, Donthini, and Microsoft Open Source 2020), consortiums of volunteers (COVID-19 India Org Data Operations Group 2020; Yang et al 2020; Zhang and Donthini 2020), and Wikipedians.…”

Ambient temperature change Vs confirmed cases of COVID-19: A machine learning model

“…Many respiratory viruses also have a speci c seasonal spread, but there is no clear evidence for the seasonal spread of coronavirus, and in fact the frequency of positive cases of COVID-19 in southern Hemisphere countries such as Latin America and Australia is reported in the summer (3). However, the seasonal emergence of the SARS virus, which belongs to the family of cold viruses, especially in spring and winter, may indicate a seasonal outbreak cycle for the spread of these viruses.…”