ALSDAIR utilizes a dozen of real-time, reliable specific data to analyze the significant factors affecting the spread of infection at the same time assessing the disease itself and recommend ways to mitigate the damage of the infection.
These factors are mainly divided into two: Pandemic Response Level and Infection-Recovery-Death Multi-thread data.
Pandemic Safety Level (PSl) incorporates Socioeconomic data (Country type, population's density and socioeconomic status percentages) and population's response towards the outbreak (authority's safety measures, public cooperation's rating and responsible individuals per unit of area) into a formula to measure the safety level of a location.
PSl's Variables are typically from reliable, latest information sources like a specific country's government-related data hub and latest research journals aside from SEDAC's GPW4, Covid19 Global Viewer, FEMA Covid19 Viewer and NASA's Covid19 Pathfinders.
Infection-Recovery-Death Multi-thread Data (IRD) uses present infection record (Infected-Recovered-Deaths with respect to time) and Transmission potential rate (Methods of transmission and rate of transmission relative to geospatial/weather data, population density, human environment and age-gender distribution data) to come up for a possible range of future infected cases and their possible descriptive locations.
IRD's Variables are more Global data-based (NEO Global Data, SEDAC's GPW4, and RADARSAT Satellite imagery) aside from transmission potential rate which can be gathered and analyzed from research results for formulaic usage. Geospatial information, specially Climate, Temperature, Humidity and Excessive Aerosol Concentration (Air Pollution), and human environment significantly affect the transmission of the infection by dictating the environment by which the infection spreads.High Population Density creates an opportunity for transmission specially in low Pandemic Safety Level areas and age-gender distribution data for assessing the infection's host preference.
IRD Multi-threaded Data formula determines the specific transmission rate of a disease in a specific geographical location and its range of near-future cases while Pandemic Safety level creates the general pandemic situation in a location. Using both will determine the spots with possibly high concentration of infected cases and establishes the general outbreak status of a specific location therefore creating an opportunity to make a series of tailor-made solutions to the specific location's outbreak.
Our families and loved ones can be considered, more or less, as pandemic frontliners and with this common emotional shackles-turned-purpose and drive to help and contribute to the world, we conceptualized and started ALSDAIR.
Community-specific problems are always observed not just during pandemic that's why CMXXXVII's aim is to cater the needs of those communities suffering without their cries being heard. The goal is to create a way to accommodate the pandemic problems of those living, mainly, in the developing countries by assessing the specific pandemic factors relative to resources and necessities, giving enough thought to the community's problem in order to craft a much more effective way of quarantine as hysteric desperation is starting to surface here in our country.
Human activity is perhaps one of the best determinants whether a disease will spread or not and having a desperate, survival-focused population will torn the community apart sooner or later. This is why we used a lot of variables connected to human activity and socioeconomic data and why we created the Pandemic Safety Level, in order to observe and learn the generals and specifics of human action relative to the pandemic mainly in a developing country where resources and necessities are running out.
We designed it as a formula first before the conceptualize application so that, at the very least, people without access to technologies can still track and analyze the results with variables using educated estimations.
Pandemic Safety Level (PSl)
(Wn)(Pd)(1/Rat+1)[(T/x)o.1] = PSl
W = Type of Country
n = 1 Developed Countries, more or less 20% of population falls on low socioeconomic status (SES)
n = 3 Developing Countries I, more or less 40% falls on low SES
n = 5 Developing Countries II, more or less 70% falls on low SES
Pd = Population Density per km^2 (NEO Population Density Data & GPW4)
1-10 = 1 We considered Pd = 1, 3 to be rural areas
10 - 100 = 3
100 - 1000 = 6.5 We considered Pd 6.5,10 to be urban areas
1000 - 10000 = 10
T = Lockdown date in relation to First infected case (days)
X = Community's cooperation/response to lockdown/quarantine
1 = No cooperation, No information access
2 = unnoticeable, with information access
3 = noticeable changes, most are still uncooperative
4 = good level of cooperation with a few number of uncooperative individuals
5 = high level of cooperation
Rat = Total Responsible Individuals per km^2, percentage of individuals inage 18-49 over total population, at decimal
PSI scores
0 - 2 = good public cooperation, good pandemic response, minimized infection growth rate
2 - 3 = medium level public cooperation, must watch for spikes in infected cases. If infection growth rate is rising, general quarantine is recommended with a duration 1 week more than the infection age.
3 - 5 = High risk, enhanced quarantine is recommended with a duration of double the infection age, necessities and resources should be carefully distributed
5 = Very high risk, enhanced quarantine is recommended with a duration of double/triple the infection age, necessities and resources are critically important and must be distributed carefully
Infected-Recovery-Death Multi-thread Score
Fc ave = Cc/[[[[PSl + (Cc)(Tra)(si/pt x c%)] - 10 ](-1)] (TrGL)x100]
FCu high = 100xIRD/20
FCu Low = 100xIRD/75
Fc ave = Future infected case average (future time duration is equal to infection age)
FCu High & Low (20 & 75) = Future total cases including the unaccounted. (the value 20 and 75 is not absolute and it may change depending on the virus analyzed, applicable only on current Covid19 )
Cc = Confirmed infected cases
Tra = transmission rate, controlled (so far, researches conclude that Tr = 9.67% for Covid19)
Si/pt x c% = susceptible population (age or gender ratio) over total population multiplied by Virus host preference likelihood of transmission.
TrGL = Transmission rate + or - the effect of Temperature and Humidity
Covid19 Transmission - Temperature & Humidity Relation
if x < 0 degrees C = - .8% Tr
0 < x => 6.7 degrees C = .8% Tr
6.7 < x => 12.8 degrees C = .83% Tr
12.8 < x => 16.8 degrees C = 1.3 % Tr
16.8 < x degrees C = -4.8951 % Tr
Humidity higher than average = -.021 % Tr
Other matrix are to be added in the near future
NASA sources/data:
https://sedac.ciesin.columbia.edu/mapping/popest/covid-19/
https://neo.sci.gsfc.nasa.gov/
https://cneos.jpl.nasa.gov/ca/
https://neo.sci.gsfc.nasa.gov/wms/wms
https://worldwind.arc.nasa.gov/worldweather/
https://worldview.earthdata.nasa.gov/
https://www.ncdc.noaa.gov/cdo-web/
https://airquality.gsfc.nasa.gov/
Other sources:
https://www.sciencedirect.com/science/article/pii/S004896972032026X
https://www.doh.gov.ph/covid19tracker
https://www.cidrap.umn.edu/news-perspective/2020/04/study-many-asymptomatic-covid-19-cases-undetected
https://news.mb.com.ph/2020/05/08/doh-7-asymptomatic-covid-19-cases-are-super-virus-spreaders/
https://www.cebm.net/covid-19/covid-19-what-proportion-are-asymptomatic/
https://www.statnews.com/2020/03/03/who-is-getting-sick-and-how-sick-a-breakdown-of-coronavirus-risk-by-demographic-factors/
https://www.statnews.com/2020/03/03/who-is-getting-sick-and-how-sick-a-breakdown-of-coronavirus-risk-by-demographic-factors/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7165096/
https://www.doh.gov.ph/2019-nCoV
https://www.medrxiv.org/content/10.1101/2020.02.22.20025791v1
https://www.researchgate.net/publication/339873481_High_Temperature_and_High_Humidity_Reduce_the_Transmission_of_COVID-19
https://www.sciencedirect.com/science/article/pii/S0048969720323792
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3551767
https://www.medrxiv.org/content/10.1101/2020.02.22.20025791v1.full.pdf
https://www.sciencedaily.com/releases/2020/05/200508083551.htm
https://www.citylab.com/equity/2020/04/coronavirus-spread-map-city-urban-density-suburbs-rural-data/609394/