Currently, the novel coronavirus, SARS-CoV-2 might be identified as the biggest world health In addition, enhancing people's awareness and implementing regulations also constituted key factors in achieving these goals of controlling disease spread. The distribution of COVID-19 disease was not only within Bangkok, the capital city, where it was first detected, but it spread to all regions of the country due to the social, economic and environmental factors such as travelling and close interaction of people. Other influencing factors involved were the international travel of Thais and foreigners around the world.
In addition, the number of reported cases varied depending on each country because the testing and contact tracing required knowledge and technology. One important factor is the high cost of testing, meaning some countries could not allocate sufficient budget for widespread testing in the community. Therefore, the number of infected cases reported by each country might significantly differ from reality. Many scientific research institutes have endeavored to forecast the number of infected cases. Hospitalization numbers, reported deaths or recovered cases in various conditions have been estimated at global or country levels based on available current data and using different modelling approaches. This paper aimed to present the distribution and pattern of COVID-19 disease and prediction of number cases in Thailand. In addition, we have attempted to forecast the COVID-19 cases in terms of the number of infected and new cases in Thailand using the dynamic SEIR (Susceptible Exposed Infectious and Recovered) model [4, 5] , and moving average prediction approaches.
The weather data, including ambient temperature, precipitation and wind speed were obtained from the official website of the Department of Meteorology, Thailand. The weather monitoring stations covered all provinces in Thailand, provided the daily mean values of each parameter used in the analysis. Descriptive statistics were analyzed, namely, minimum, maximum, mean, median, standard deviation in quartiles 1 and 3. We collected published health data from the Ministry of Public Health, which is available from the official website (http://covid19.ddc.moph.go.th/th) [6] . The Spearman's correlation of weather factors with the daily reported COVID-19 cases was analyzed using SPSS (version 18). The analysis employed a spreadsheet for the susceptible and infection rate, recovered rate and death rate. The daily temperature and cumulative infected cases were analyzed for their association at the outbreak period.
The prediction was performed using the dynamic SIR (Susceptible Infection and Recover) concept model [7] , a well-validated model, particularly useful for analyzing the spread of The causal loop was converted to a computer diagram using a license STELLA Software to generate the equation as a baseline study using the current parameters for Thailand, such as the rates contact, infection and recovery. Up to this stage, the model focused on the number of infected cases with intervention measures.
In addition, we employed the moving average approach from the previous detected cases for prediction the infected cases by three categories, namely: two, three-and five-days moving average. The pattern or trends of detected cases were used for future prediction. The RMSE (Root Mean Square Error) was used to determine the goodness of fit of each model mentioned above.
This analysis shows the evolution of COVID-19 cases in Thailand from the first case until 6
July 2020. The majority of positive cases were male, at 56.7%. The average age on positive individuals was 42.5 and 35.7 for males and females, respectively. The maximum age for males was 84, whereas for females was 80 years. The minimum age was found at only five months of age [14] . The timeline of COVID-19 cases in Thailand and critically related events is illustrated in Figure 2 . The first phase was initiated from the first infected case in Thailand 22 January until 
During the study period, descriptive statistics were used to analyze data ( During the studied period, almost regions were summer season, however, there was some precipitation in the Southern region of Thailand. Due to the variation of precipitation across the country, the related coefficient of variation was high (61.8%), so this parameter was not analyzed to find any correlation with COVID-19 cases. We found a significant association (p-value <0.001) as presented in Table 3 . April 2020, if intervention was not strictly performed [19] . This was also the reason the authors investigate the prediction with or without intervention enforcement.
The causal loop diagram shown in Figure 1 was transferred to the diagram in Figure 4 and then converted to equations (1 to 4) using the SEIR approach. For example, the number of positive cases depended on the number of individuals with COVID-19 and their contacts in each period. The SEIR model has been applied in the study of many infectious diseases [20, 21] . The equations of the SEIR model are shown below. E is the number of the exposed population is the probability of changing from E to I ……………………………………………………… Recovery (4) is the probability of changing from I to R
We simulated the base run using the current situation and using the implementation approach of intervention, namely, reduced contacting rate. At the base run study, 85% of no close contact or reduction of exposure (social distancing) was performed according to the enforcement of interventions such as the State Emergency Act, curfew, closing many high transmission risk places (indoor stadiums, department stores, restaurants and others). The result of predicted cumulative positive cases compared with the actual cumulative cases is presented in In addition, the authors used the moving average model for prediction; namely, two, three-and five-days moving averages. Johns Hopkins University used five days moving average for prediction [22] . In addition, other researchers also employed this technique to forecast COVID-19 cases [23] . The results of two, three-and five-days moving averages are presented in reported by other researchers [5] . Community health volunteers play a strong role in the Thai health system and they can cover the whole country. The partnership among the involved and responsible organizations in each area and individual cooperation were the key role for disease spreading control.
From the records, COVID-19 in Thailand was mainly found in Bangkok, which was the original source of spreading. Bangkok is the center of various aspects in this country and serves as the gateway for foreigner visitors to Thailand. Moreover, foreigners typically visit Bangkok because of the available tourist attractions. In addition, many international and local flights are offered to many places that are convenient and comfortable [17] . In addition, health volunteering in the capital city cannot be easily performed due to the complexity of the health system, whereas health volunteering in rural or other provinces was simplified regarding the duties of volunteers.
A strong correlation between temperature and the number of COVID-19 confirmed cases, we found a similar result of average temperature and confirmed cases in Jakarta, the capital city of Indonesia located in the same tropical climate zone [24] [25] [26] [27] period of days) also provided better results than a longer moving average. However, the moving average approach is only suitable for short term prediction, whereas SEIR modelling can be used for longer prediction. Controlling the disease spread was strictly enforced through the collaboration of central and local governments, partnerships among stakeholders and people in their community. This highlighted that social distancing and personal hygiene and should be performed rigorously and consistently across sectors [4, 30] . Isolating confirmed cases also played a key role in reducing daily spread of the disease [5] . Moreover, the Thai government employed health volunteer at the community level to act by isolating at risk members in the 6. Acknowledgment