100 word summary: https://docs.google.com/document/d/11qfJl0RotcAdmcNAGvOwmew2mmJ77NSSsFPU88wyHvw/edit?usp=sharing

Abstract: COVID-19 is a new type of coronavirus disease which is caused by severe acute respiratory syndrome coronavirus  (SARS-CoV-2). It originated in China in the month of December 2019 and rapidly started to spread within the country. On 31st December 2019, China warned to the World Health Organization (WHO) about this new infectious disease. Since then, it has spread to most of the countries around the globe. During the beginning of the pandemic, there was a rise in believing that it would go away during summer days arguing that the new novel coronavirus will likely not survive in high temperatures environments. That reasoning led us to think if environmental factors plays that kind of role in this type of situation and what if we could devise a form of environmental data tracker that would allow governments and scientist from all the world to analyze and examine environment-related data to make better decisions.

Introduction:

COVID-19 continues to inflict more havoc globally. As the number of infections keep surging high, deaths continue to increase at no small rate, active cases continue to scare the world and a possible vaccine invention seems to be several months  away from now. About its treatment, the world is quite unsure, leaving us all in a tremendous uncertainty. This pandemic has touched every activity in this world and has sent the world experts in health and data science to work to find possible solutions to this issue. While the experts in economics and policy makers have concerned themselves with the mitigation of the impact of COVID-19 on their nation’s economy, its people and their businesses. Researchers have investigated the role of pre-existing conditions such as hypertension, diabetes, ischaemic heart diseases etc, on COVID-19 deaths and the environmental factors that influences COVID-19 deaths and the role of climate to related deaths and new confirmed cases. The relationships between climate and infectious diseases have long been established as seasonal variations and weather susceptibilities are well known to explain some variability in many infectious diseases. Statistical, process-based, and landscape models are the key categories of models for the estimation of potential climate effects on infectious diseases. These three model categories address very different problems. Statistical models for instance, require that the relationship between the current geographic spread of the disease and actual localized climate specific conditions be inferred empirically.

There have been the confirmations that ultraviolet light has a sterilizing effect, because the radiation damages the virus’s genetic material and its ability to replicate. Recent findings showed us that temperature and irradiance of COVID-19 will decide the trajectory of the pandemic at warmer regions as well as whether rising temperatures will change direction and have consequences on public health policy. They also showed that case and death counts at higher temperatures (>14 °C) when aligned for the epidemic stage had significantly lower rates of growth. Although, in warm weather locations such as Indonesia, Iran, and Peru, COVID-19 have also proven lethal and create wider concerns on the environmental factors. A work conducted by B. Oliveiros, L. Caramelo, N. Ferreira and F. Caramelo on the “role of temperature and humidity in the modulation of the doubling time of COVID-19 cases”revealed that, temperature and humidity explain a total of 18% variability in the disease doubling time while the rest (82%) may be assigned to general health policies, containment initiatives, massive population dispersion, and transport.

With our Etracker we hope to determine the number of new infections due to environmental factors in New York and to predict future occurrences of new viruses around the globe. 

2 Materials and methods

2.1 Study area:

The study covers the New York City area making a focus in Manhattan and Brooklyn.

2.2 Data

Daily data on new cases of COVID-19 from March 20, 2020 to April 9, 2020 were collected from the official website of Our World in Data. Daily satellite climate data covering the same study period on maximum temperature, humidity, precipitation and maximum wind speed were obtained from the official website of the National Aeronautics and Space Administration’s (NASA) Prediction of Worldwide Energy Resources (POWER) project. As a second resource we used Meteomatics to check on weather variables and to compare that data to POWER.

2.3 Methodology: Using gatsby leaflet starter we started to create a COVID-19 dashboard that allows you to compare socio-economics metrics but also including a strong emphasis in environmental influences. Inside the tracker, by using crossed data from different resources you would get to do a profound analysis and predictions not only with COVID-19 but it would help to predict future outbreaks from different viruses. This would help authorities, for example, to know in which communities it would be more helpful to allocate resources and infrastructure.

Since we are developing an interactive API, when it finish, it would not only be understandable for scientists but also for policy makers and people with no knowledge in epidemiology.

2.4 Tools: Planet Explorer

POWER Project

Meteomatics API

An intel inside processor with of 1GB RAM :)

Gatsby & Leaflet

Open Street Map

Results: We conclude that a tool of this type within the reach of governments and scientists could help them make a comprehensive analysis pertinent to the influences of environmental factors that may have some impact on the spread of COVID-19 and of course, new viruses. We think based on our studies that it would give them a new approach when writing scientific reports by taking into account not only socio-economic but also environmental indices.