A full stack application built using Firebase, and Google Cloud Platform to detect high mental-health risk clusters affected by the Covid-19 Pandemic.
=> Covid-19 takes a toll on mental health; When people experience traumatic and often unexpected events, the reaction is usually a combination of fear and distress.
=> Many resources focus on detection of physical well-being, but not on mental well-being. E.g. The impact of Covid-19 is often measured by the number of fatalities, number affected, and recovered; But how do we measure the mental toll?
=> We can detect suicide / depression clusters; clusters of population that are most susceptible to suicidality or depression.
*All of these clusters lead to risk of sudden loss in population, panic, and pain:
=> Point clusters (same geographic location within the same time period)
=> Mass clusters (across different geographic locations but within same time period)
=> Echo clusters (same geographic location, at a later time period)
=> Certain factors increase the risks of suicide;
- Age: older people are particularly vulnerable during this pandemic, and already suffer from high rates of loneliness, which is strongly associated with greater symptoms of depression, and mortality
- Physical morbidities: evidence shows inadequate physical healthcare can lead to greater risk of mental illness
- Number of deaths by Covid-19 confirmed in the area: exposure to another person’s suicide, be it acquaintances, family, or person in local viscinity - can drastically increase risk of suicide clusters.
- Number of active cases of Covid-19 in the area: large no. of cases leads to increased anxiety and fear, and due to people feeling ‘unsafe’ about going outside, staying indoors leads to increased loneliness / isolation. These all lead to increased risk of suicide/depression.
=> We gather data using the Google Cloud BigQuery Public Dataset: Covid-19 Open Data, using BigQuery!
=> The dataset provides access to critical information quickly, easily & is automatically retrieved.
The structure of this list is as follows: Previous dataset column name : COVID-19 Open Data column name province_state : subregion1_name country_region : country_name date : date latitude: latitude longitude : longitude location_geom : location_geometry confirmed : cumulative_confirmed deaths : cumulative_deceased recovered : cumulative_recovered active : current_cases (NOTE: Calculated as [cumulative_confirmed-cumulative_recovered-cumulative_deceased] where none of these values are NULL) fips : subregion2_code admin_2 : subregion2_name combined_key : location_key
By mapping out high-risk clusters using metrics provided by the automatically retrieved Covid-19 open data, we can:
- Reallocate scarce medical resources,
- Optimize the stationing of first-responders and police for injury / emergencies.
*Our application will act as a virtual ‘cluster response group’; it helps to effectively identify and respond to mental high-risk clusters.
Web application
Effectively relays information about 'high mental-health risk clusters' to first responders and police dispatchers. Shows active alerts for areas that exceed a risk threshold.