Covid-19 Spread Summary Thailand
We have computed all modeling data and came up with the following results. Please understand we do not publish data more then 5 weeks in advance and our Reports are only available on Request.
The 2nd Wave of Covid-19 triggered by flights from and to Thailand, South Korea and Japan
Coronavirus Spread Simulator for Thailand (Data used)
All data that have been retrieved and have been feeded to five different Linux high-powered servers (CSV to MySQL) all based in the German city of Wiesbaden. We process all modelling on different formula’s and data with countries in the Association of Southeast Asian Countries (ASEAN). Currently we have direct-access to 16 different Health organisations globally and our Meta Retrieval system downloads those CSV tablrs and inject their data in our SQL Database tables for automatic processing of all Data. The data for Thailand is very limited and we base our numbers mainly on ASEAN Countries including data that comes from the governments of Singapore, Malaysia, Indonesia, Vietnam and the Philippines. We record all Covid-19 spreads for each province or state in those other ASEAN countries and then generate fullreports that takes around 18 hours to compute.thailand country is not exist in our data.
The original source-code of SentosaXchange was based on mathematical calculations that we developed for the Pacific Asia Travel Association for their annual PATA Travel Mart (PTM) and included PTM’s in India (2015), Indonesia (2016), Macao (2017), Kazakhstan (2018) and Malaysia (2019).
Our calculations are based on 4 weeks ahead and we do not publish any data on the World Wide Web after 4 weeks.
|Starting Thai Population||69,928,453|
|Immune Population (not enough data)||0|
|Infection Rate (3%) due to numbers we matched from Malaysia - Indonesia and the Philippines. We left Singapore out due to high efficiency in tracing community spread.||3|
|Mortality Complication (ICU's that might get overloaded) - Added 20% specially for the Thai provinces in North Thailand - Northeast and the Deep South of Thailand due to very limited ICU units||10%|
|Iteration Count (Weeks)||8|
|Thai Population at Risk||69,928,453|
|Total Thai's Infected with Covid-19||23,781|
|Total Thai's Recovered from Covid-19||22,558|
|Total Thai Deaths||1,223|
|Thai Population Alive after Covid-19 Pandemic||69,927,230|
|Date||Day||New Infected||Total Infected||New Deaths||Total Deaths||Population Alive|
Total Covid-19 Infections via Deaths in Thailand
Covid-19 Disease Severity Chart for Thailand
Covid-19 Disease Hospitalization Chart for Thailand
We started in this model with an infection of 10 on the 28th of February 2020. Mortality rate is 2.3% due to an overload of hospital ICU units. We have taken Bangkok out with 10 million to only compute Hospitalization outside Bangkok