imktd8

[imktd8]

I agree with you all. In case of a small sampling group, the combination of non-identifiable data can identified participants, for example;
Gender: Female
Blood Group: B
Age: 36
Year of Birth: 1984
Province: Chonburi
Occupation: SAP Consultant
Education: Master Degree

Remark. In my opinion, using non-identifiable can protected health information. For example, age, gender, province, diagnosis. These decrease issues in privacy information in healthcare because they can not determine the patient.

[imktd8]

I agree with you all. In case of small sampling group, the combination of non-identifiable data can identified participants, for example;
Gender: Female
Blood Group: B
Age: 36
Year of Birth: 1984
Province: Chonburi
Occupation: SAP Consultant
Education: Master Degree
Remark: In my opinion, to user non-identifiable can protected health information. For example, age, gender, province, diagnosis. These decrease issue in privacy information in healthcare because they can not determine the patient.

[imktd8]

I agree with you all. In case of small sampling group, the combination of non-identifiable data can identified participants, for example;
Gender: Female
Blood Group: B
Age: 36
Year of Birth: 1984
Province: Chonburi
Occupation: SAP Consultant
Education: Master Degree

PS. In my opinion, to user non-identifiable can protected health information. For example, age, gender, province, diagnosis. These decrease issue in privacy information in healthcare because they can not determine the patient.

[imktd8]

If I am analyzing survey responses with regard to the use of bednets for prevention of malaria and I have no data about why they are not using bednets. To correct the data to analyze, the qualitative study is selected to study. For the method I will select to collect the data are in-depth interviews and focus group discussions. These help to get the reason and behavior why they do not use the bednets. In addition to decrease the bias, I will offer the electronic survey to collect the data. This is because some people may be shy to inform the information which they think it is the privacy data.

[imktd8]

In case of replacing an old technology with a new one that is easier to use, as my work in the enterprise system implementation. The first step is to do the assessment process. This step will help to know the as-is problem and the pained point, for example, the manual process which is not simple, take times/work load and duplicate work. In my opinion, only ease of user is not enough. The user will be happy with the process or system if there are both ease of user and usefulness. The ease of user will effect to received the usefulness and the new technology should suitable and user friendly to use. These will impact to user behavior to use and receive the system.

[imktd8]

For some specific ‘external variables’ in TAM model, they not the the manipulated factors as part of an experiment, but they affect to usage behavior and may exert some influence on the dependent variable under study.

The external factors that I am interesting are user/system experience. This effect to perceived ease of use or perceived usefulness of a new technology. For example in Thailand, there is the application which used to vaccine registration. This is deploy to Thai people who difference in age. The young people who can user the smart phone will accept that this application is easy to use and usefulness. It decreases time in vaccine request process, but in the older people who lack of knowledge and experience in technology or smartphone usage. They may can not receive the ease of user and usefulness and this may impact to the amount of vaccine request register.

[imktd8]

For some specific ‘external variables’ in TAM model, they not the manipulated factors as part of an experiment, but they affect to usage behavior and may exert some influence on the dependent variable under study.

The external factors that I am interesting are user/system experience. This effect on perceived ease of use or perceived usefulness of new technology. For example in Thailand, there is an application used for vaccine registration. This is deployed to Thai people who differ in age. The young people who can use the smartphone will accept that this application is easy to use and usefulness. It decreases time in the vaccine request process, but in the older people who lack of knowledge and experience in technology or smartphone usage. They may not receive the ease of user and usefulness and this may impact the amount of vaccine request register.

[imktd8]

For some specific ‘external variables’ in TAM model, they not the the manipulated factors as part of an experiment, but they affect to usage behavior and may exert some influence on the dependent variable under study.

The external factors that I am interesting are user/system experience. This effect to perceived ease of use or perceived usefulness of a new technology. For example in Thailand, there is the application which used to vaccine registration. This is deploy to Thai people who difference in age. The young people who can user the smart phone will accept that this application is easy to use and usefulness. It decreases time in vaccine request process, but in the older people who lack of knowledge and experience in technology or smartphone usage. They may can not receive the ease of user and usefulness and this may impact to the amount of vaccine request register.

[imktd8]

For some specific ‘external variables’ in TAM model, they not the the manipulated factors as part of an experiment, but they affect to usage behavior and may exert some influence on the dependent variable under study.

The external factors that I am interesting are user/system experience. This effect to perceived ease of use or perceived usefulness of a new technology. For example in Thailand, there is the application which used to vaccine registration. This is deploy to Thai people who difference in age. The young people who can user the smart phone will accept that this application is easy to use and usefulness. It decreases time in vaccine request process, but in the older people who lack of knowledge and experience in technology or smartphone usage. They may can not receive the ease of user and usefulness and this may impact to the amount of vaccine request registers.

[imktd8]

In the experiment study, the criteria for good treatment are defined are examine for efficacy, effectiveness and efficiency. These three terms related to the outcome measure in experimental study, including of “Efficacy”, “Effectiveness” and “Efficiency” can measure as below.
1.Efficacy : This studies investigate the benefits and harms of an intervention under highly controlled conditions. It requires substantial deviations from clinical practice, including restrictions on the patient sample, control of the provider skill set and limitations on provider actions, and elimination of multimodal treatments.
2.Effectiveness : This studies (also known as pragmatic studies) examine interventions under circumstances that more closely approach real-world practice, with more heterogeneous patient populations, less-standardized treatment protocols, and delivery in routine clinical settings.
3.Efficiency :This studies provide the evidence base to make decisions about the introduction of innovative interventions while discouraging the use of inefficient interventions.

[imktd8]

For the research that related to my research topic is to study abount the relation of online search and covid-19 disease.
1. The title of the paper : Are online searches for the novel coronavirus (COVID-19) related to media or epidemiology? A cross-sectional study
url: https://reader.elsevier.com/reader/sd/pii/S1201971220304641?token=9F9511F96DDE2A1FBB0B2684176EBD06F28C70ED72ED538A88DF436D4F57B34EAA1EB2FAF3111B46A7D8CC2362BA2BE7&originRegion=eu-west-1&originCreation=20210504043312
2. Main objectives of the study : To determine if online searches for COVID-19 related to international mediaannouncements or national epidemiology
3. Main exposure variable of interest: The online COVID-19 searches and the announcements by the World Health Organization (WHO).
4. Main outcome variable of interest: Online searches for COVID-19 in Europe are not correlated with epidemiology but stronglycorrelated with international WHO announcements.
5. Limitations of the study:
– Lack of the demographics study (i.e. age, gender, location or education level)of online searches and COVID-19 cases were not taken intoanalysis.
– There are not vary media which is studies in this study. This focused only on WHO announcement.

[imktd8]

1.The title of the paper : Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea
2. Main objectives of the study: To provide a new approach for query selection through the exploration of contextual information gleaned from social media data and evaluate he possibility to use these queries for monitoring and predicting influenza epidemics in South Korea.
3. Sampling method used in the study
3.1 Data: The query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014
3.2 Method: To select queries related to influenza epidemics, several approaches were applied:
(1) exploring influenza-related words in social media data
(2) identifying the chief concerns related to influenza
(3) using Web query recommendations.
4. Limitations of the study:
4.1 The changes in Internet usage rates and health information seeking rates may constitute a somewhat central limitation on the use of search query data.
4.2 Noise from irrelevant information and uncertainty regarding the representativeness of the sample of health information seekers are also significant limitations.

[imktd8]

1.The title of the paper : Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea
2. Main objectives of the study: To provide a new approach for query selection through the exploration of contextual information gleaned from social media data and evaluate he posibility to use these queries for monitoring and predicting influenza epidemics in South Korea.
3. Sampling method used in the study
3.1 Data: The query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014
3.2 Method: To select queries related to influenza epidemics, several approaches were applied:
(1) exploring influenza-related words in social media data
(2) identifying the chief concerns related to influenza
(3) using Web query recommendations.
4. Limitations of the study:
4.1 The changes in Internet usage rates and health information seeking rates may constitute a somewhat central limitation on the use of search query data.
4.2 Noise from irrelevant information and uncertainty regarding the representativeness of the sample of health information seekers are also significant limitations.

[imktd8]

For the other confounders in my sight is the technology capability, there is a gap between people in vary age group. They have difference experience in technology usage. In the young adults, it is more easier to use, understand, accept ease of user and usefulness of the technology than the other age groups, but this issue can be solved by giving the training or knowledge to them.

[imktd8]

Research title: Pandemic Correlation and Prediction Model of New COVID-19 Epidemics
in Thailand Using Internet Search Engine Data

Variables:
– Sex (categorical) : summarized as proportion
– Age (continuous) : summarized as mean(SD)
– Nationality (categorical) : categorical variable – proportion
– Province of isolation (categorical) : categorical variable – proportion
– Signal predictor (categorical) : categorical variable – proportion
– Area (categorical) : categorical variable – proportion
– Risk (categorical) : categorical variable – proportion
– Infected Amount: continuous variable – summarized as mean(SD)

[imktd8]

#No.3 A significant test result (P£0.05) means that the test hypothesis is false or should be rejected.
If P-Value is less than the chosen significance and then we reject the null hypothesis to support the alternative hypothesis. It does not imply a meaningful or important difference. A low P-value may conclude that there’s statistical evidence to support the H0 rejection, but it doesn’t mean that the alternative hypothesis is true. For example, if we set a significance level at 0.05, it means that there is a 5% chance of rejecting the main hypothesis. That assumption is true (wrong decision)

[imktd8]

1. Introduce yourself about your background, what kind of work you are doing that related to statistics.
I work as an SAP consultant. The main jobs are to implement the SAP/enhancement project or support the enterprise system in the oil and gas area. The kind of work that I think maybe related to statistics is the project assessment and estimation phase. These phases have to use historical data in the decision process.

2. Have you ever learned or applied statistics in your work related to data analysis or statistical analysis. Please share your experience.
Surely, I have ever learned and applied statistics in my work. For an enterprise system likes SAP, there are a lot of business transaction data that are called ‘Big Data’. To optimize or improve the process, it’s necessary to summarize and analyze the data to find insight. Then the statistics method and tool are used in this work process.

[imktd8]

add the picture link kha: https://photos.app.goo.gl/6TwV6q8teHh591E68

[imktd8]

The incorporating interventions that I select to implement into the Influenza SIR model are vaccine campaign, wearing mask campaign, and treatment. For adding the interventions into the model structure, please see the attached figure.

For the characteristics of the interventions as below:
1) “Vaccine Campaign” will impact the Susceptible group. This program helps patients to be more aware and take care of themselves away from influenza and help reduce the chance of infection.
2) “Wearing Mask Campaign” helps people take care to wear masks to prevent infection and infect others. As well as reduce the chance of infection.
3) “Treatment” will help the infected person to recover from illness.

[imktd8]

For the model structure that I am interested in and select to study for influenza is “SEIR” model. I divide the Thai population into 4 compartments that represent different disease states as below:
1) Susceptible (S): representing who have not been infected or vaccinated and are therefore fully vulnerable to infection.
2) Exposed (E): representing who have been infected, but who have not yet progressed to become infectious (i.e. able to infect others)
3) Infectious (I)
4) Recovered (R): representing who are no longer vulnerable to infection with the same virus type. (they have been infected and recovered or have been effectively vaccinated)

The parameter of this model can descript as below:
1) Infection rate
2) Incubation rate
3) Innate mortality rate
4) Immunity rate
5) Population rebirth rate
6) Amout of population
7) Effectiveness of the campaign to educate (4 media types: Video, Text, Static Picture, Virtual Reality)

Remark: Source of data are from the National Notifiable Disease Surveillance Report of the Bureau of Epidemiology at the Ministry of Public Health.

[imktd8]

1. Why was the author interested in investigating the suicide problem in Thailand during the time?
> The author interested in investigating the suicide problem in Thailand as :
– The number of suicides in Thailand has been increasing over the last decade (from 2005 to 2011) and rising since 2011, higher than 6 suicides per 100,000 inhabitants.
– It has probably been other factors impact to suicide rate, both the economic factors and the social factors or some factors which may have a relationship with suicide rates can be found by using multiple regression analysis.
– There are data sources to study, for example, suicide rate from the Ministry of Public Health, household income/debt from National statistics Bureau of Thailand and alcohol consumption rate from the Center of Alcohol Problem Research, etc.

2. Each of the students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
> As the study objective, this paper attempted to explore the factors leading to suicide rates in Thailand. Alcohol is one potential risk factor that can contribute to the suicide rate. For model 1 & 2 which using multiple regression analysis, there are difference condition of age in ALR20 & ALR15 and the regression result show that the suicide rate in Thailand was significantly driven by alcohol. It is found that alcohol prevents self-control and brings out courage for people under severe depression to commit suicide. In my opinion, alcohol drinking and addiction have been linked to health problems and other consequences, for example, social, financial, health, and work problems – the increased risk of suicide.

3. How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
> The model is used to analyze the factor (both independent & dependent variables) which may have a relationship with suicide rates by using multiple regression analysis. There are 2 models with economic factors & social factors. For Thai suicide problem, the results show significantly (ex. alcohol, household income, divorce rates) and no significant (ex. unemployment) impact on suicide rates of economic & social factors. This result helps to imply and apply to the government to improve or release the law or policy which helps to decrease the suicide problem in Thailand in the future.

[imktd8]

(1) For the possible reasons the location have not been incorporated in the epidemiological research as the other components are:
1) Disease type: In the epidemiological research of noncommunicable diseases, for example, cancer, cardiovascular diseases, pulmonary diseases, and metabolic diseases which focused on particular risk factors, the location may not the main factor to study.
2) Knowledge & Skill: Some of the researchers lack knowledge, skill, and experience of statistics and tools used in spatial or geographical epidemiology or geographical information system.

spatial epidemiology is considered as an interdisciplinary science because it emerges the intersection of multiple scientific disciplines including spatial science, location-based technology, and health data to investigate the effects of behavioral, environmental, psychosocial, and biological factors on health-related topics.

(2) As ‘‘Place’’ can be applied as a surrogate for the interaction between genetic factors, human behavior (Social or individual), environment (location or temperature). In communicable diseases, for example, COVID-19, Ebola, Flu, Tuberculosis, to focus on public health research, place/time/ behavior (individual or together) can imply the aetiological hypotheses.

[imktd8]

@Ameen your dashboard has a vary of charts and graphs which interesting. It may easy to decision at first sight if you select to use a different color for each category. Overall, this is an interesting and informative dashboard. #Cool

[imktd8]

@Tullaya Your dashboard has a nice and clear theme. I like it. It easy to select the data, the color is different which help to separate for each category. For the “Day” data, it’s not descript the start date of count.

[imktd8]

For my final dashboard of COVID-19 pandemic situation Dashboard, please see as below link:
https://app.powerbi.com/view?r=eyJrIjoiMTViZmQxNzAtMGE0MS00Njk1LWE3MWUtMjdlMGYxMTQzNmFiIiwidCI6ImQzMDNkYjhjLWQwMzQtNDAxMC1hN2VlLWM0N2JlYmY1YmNhMyIsImMiOjEwfQ%3D%3D
I select map, graph and chart which easy to use and not complicate to understand. To rank the top 5, use slicer to compare cases for each country, show the % of cases, help to know about the amount of confirmed, recovered and death cases easily.

[imktd8]

Please see my COVID-19 pandemic situation Dashboard as below:
My Dashboard
I select to create a dashboard that consists of Map, Card and multi-row card, Funnel Chart, Treemap and Scatter for 1 tab. This can give a COVID-19 pandemic situation in the context of confirmed, recovered, and death cases volume by continent and country and confirmed case prediction in the next 30 days.

[imktd8]

For my Dashboard, please see URL as below:
https://app.powerbi.com/view?r=eyJrIjoiMGIxNjY1MTgtNDk3OS00NDQxLWIyZWEtYzIyZWMwNzIxMTFjIiwidCI6ImQzMDNkYjhjLWQwMzQtNDAxMC1hN2VlLWM0N2JlYmY1YmNhMyIsImMiOjEwfQ%3D%3D
The dashboard consist of several interactive graphs/charts types with 3 pages:
1. Covid-19 World Situation Report
2. Covid-19 Forcast for next 30 days
3. Covid-19 Daily Confirmed Case (Top 10 Ranks)

[imktd8]

Please see my dashboard via this link as below:
https://app.powerbi.com/view?r=eyJrIjoiMWI2NDg2NmYtOTNmMS00ZjQwLWJhNzYtNGRkNGFhODJkNWQwIiwidCI6ImQzMDNkYjhjLWQwMzQtNDAxMC1hN2VlLWM0N2JlYmY1YmNhMyIsImMiOjEwfQ%3D%3D
There are 3 pages that show about:
1. Covid-19 world report that includes with the continent, country, covid-19 cases, map and top 5 of confirmed 7 death cases by continent & country.
2. Covid-19 compares cases by Top 20 country.
3. Covid-19 world report with GDP of Top 20 country.

[imktd8]

All above data visualization dashboards for COVID-19 disease are amazing and I would like to share this one:
Information Is Beautiful (URL:https://informationisbeautiful.net/visualizations/covid-19-coronavirus-infographic-datapack)
I like this dashboard because there are 10 graphics which show in vary patterns on this page illustrate a wide range of COVID-19, for example, the seriousness of symptoms, daily growth of outbreak, incubation periods, the infections and fatality rates by country. They are colorful and cleary. Although the stats are updated regularly but not daily.

[imktd8]

Thank you for your answers, clearly and I love the idea to add the GIS map to the location & hospital.
excellent kha!:)

[imktd8]

Thank you for your question kha Khun Penpitcha -/\-
For 1st question: I have observed that in some areas, number 43 Wiang Haeng, for example, RR score between males and females is quite different.
–> From fig.2: b refer to male’s RR in each district and c refer to female’s RR in each district. For No. 43 Wiang Haeng: b figure (RR = 0.95 – 1.05) and c figure (RR > 1.5). This is not quite different. As we discuss in Line app ^^ Although male & female live in the same district, the results of the spatial analysis by genders showed that the RR of lung cancer mortality for males over the whole study area ranged from 0.549–1.555 and that of females ranged from 0.525–1.978. In figure 2, (b & c) you can see the result by gender. For the disparity in the results was probably due to the differences in the design of the studies used to estimate the risk including of other factors (ex. data of population, environment, occupation, landscape, activity (drink or smoke), age, family history, etc.)

For 2nd question: Can BYM model explain this result? and How?
–> The aim of this study was to identify risk patterns of lung cancer mortality across 81 districts spanning the 6
northern provinces of Thailand. Then BYM is used to estimate the relative risk of lung cancer mortality includes both spatial heterogeneities typically represented using the aggregated neighbors of each district and uncorrelated spatial heterogeneity follow this formula: log(θi) = α + ui + vi
– α is the intercept of the relative risk
– ui and vi are the correlated and uncorrelated heterogeneity components, respectively.
ui is assumed to apply the spatial correlation since the relative risk estimation in each i is dependent on the neighboring areas.
For the disparity in the results was probably due to the differences in the design of the studies used to estimate the
risk too kha.

[imktd8]

Thank you for your question kha Khun Thanachol -/\-
For 1st question, I wonder that relative risk for lung cancer among male was in a different area compare to female?
–> From table3: the relative risk of male in each district is different from female. For both genders, the risk patterns of lung cancer mortality indicated that high risk in the West of Northern Thailand, with females being at a higher risk than males.
In my opinion, it’s normally that relative risk for males and females is the difference. In addition to the area they live (each district), there are other factors unknown before and I think that this’s an effect on relative risk value kha.

For 2nd question, Although it is related to the environment, why males in Chiang Mai had a higher risk for lung cancer meanwhile females in Chiang Rai had a higher risk for the same disease?
–> From table3: show the highest risk of lung cancer mortality in each province by gender. This is good question kha 🙂
This disparity in the results was probably due to the differences in the design of the studies used to estimate the
risk. Not only gender & geographical patterns, but also other factors like air pollution, water, occupation, landscape, activity (drink or smoke), age, family history, Mental health, etc. can impact the relative risk in other way.

[imktd8]

Thank you for your answer kha khun Chanapong -/\-
For the first answer, I agree too to provided prevention knowledge to older people and their family members.This helps them to avoid and aware of fall problem.
For the second answer, I agree with you that Village Health Volunteer may not be a choice for detecting falling older people and from your experience, smartphones or mobile phones with integrating GIS technology may help to send the emergency notification to the hospital or emergency contact point. This can show the location of a falling patient who stays alone and increase the potential to get treatment in a short time.

[imktd8]

Firstly, I’m so sorry for my late comment kha -/\- and Thank you for sharing. I did a report about TB last term and I had just known that TB is a communicable disease that is a major cause of ill health, one of the top 10 causes of death worldwide nowadays. This study is to investigate the social behavior patterns who have developed TB disease and apply with GIS technology, so this’s an interesting and valuable study and can be continued in Thailand context. For the population in this study, there are three groups, what do you think if Latent tuberculosis will be included in the study? and study conclusion, it makes me think of contact tacking which is used in COVID19 currently, what do you think if use contact tracking to support patients with suspected or confirmed infection?

[imktd8]

Firstly, I’m so sorry for my late comment kha Khun Tullaya -/\- and Thank you for good presentation 🙂 As CDC report… Dengue is common in more than 100 countries around the world. Forty percent of the world’s population, live in areas with a risk of dengue. In my opinion, this study is very interesting, it may apply to Thailand context for future work because Dengue is endemic in Thailand, and risk is present in both urban and rural areas every year. Back to this study, Is it possible to study the relation between rainfall in each area with (Dengue) cases that occur for each year? (because Peak transmission typically occurs during the rainy season.) and if you were a researcher who has to improve research design and data collection from this study, what anything do you want to change or how do you do?

[imktd8]

Thank you for sharing, this study is good to learn about using spatial analysis and GIS in health project. Figure 1 shows the process flow diagram with steps for the decentralized planning process. In my opinion, in process improvement, pained point finding and impact analysis are the pre-step to start the project, if you were a researcher, what step/detail do you want to add or change in this process flow? and from research limitation, what factor do you think that can adjust for the better study, for example, population (a small number of interviews), stakeholder management, is there any else in your opinion?

[imktd8]

Thank you for sharing. In Thailand, we are in the aged society which falls are the most common unintentional injury among the elderly and are an important public health problem today. From this study, the application of GIS which can help to identify risk-related hot-spots to reduce falls among older adults is interesting. If you were a researcher of this study, how will you do with the highest-risk area (to decrease fall-case)? I think this a good idea to apply in Thailand context. In Thailand, there are a lot of elders who live alone in a rural province and stay far from the hospital. The fall-related EMS calls may not serve as well, Village Health Volunteer (อสม.) may is the better choice. How do you think about this?

[imktd8]

Thank you for your sharing, this study is interesting in TB context which integrates geographical aggregation of TB cases.
TB cases can transmit in a community, How’s about applying contact tracking to monitor & control epidemic?

For the method, Clinico-demographic details were extracted from treatment cards. There are TB patient data only, but Latent Tuberculosis data is not included. It is possible to design data collection to collect data extensively.

Form Pyae Phyo Aung’s question, although geocoding is the strengths of this study (no selection bias) but it’s a limitation too (some case may fail to geotag). To solve this problem, Is there any technique or technology to represent geocoding?

Many thanks & cheer kha 🙂

[imktd8]

Thank you for your sharing, your presentation encourages me to think about a lot of variables that it’s interesting to research for future work.
These’re some my ideas to share with regards kha,
– Research methods, this study use Correlation Coefficient which is a technique for investigating the relationship between two quantitative, continuous variables. This research study the relative of humidity and temperature that relates to the influenza epidemic. For future work, there are many factors that may impact the study result, but we don’t know. It can use
Spatial Analysis to find the unknown relative risk for each area. This may help to design the disease surveillance plan to control the epidemic or disease tracking system.
– Data collection, I think like Khun Ameen. in my opinion, I think that this period is a little short term to find a pattern
or relation exactly. It may have other factors that impact the result, for example, gender, age, location?
– For future work, air pollution in Thailand is so critical with PM2.5. It challenges to study PM2.5 impact on other diseases, for example, stroke, and heart disease.

[imktd8]

My video presentation:
https://drive.google.com/file/d/1Bo6h1kV5WLbzbLJj6jFWZzK_pfFDe1qF/view?usp=sharing

[imktd8]

Special thanks for all of your comments ka -/\-
This’s so useful and can help me to select a suitable paper for the next step ka 🙂

[imktd8]

I agree with K’Thanachol ka. It is quite difficult to choose. For me, both of them are interesting, although they are not exciting, but there are good of GIS knowledge, data collection & research method. For the first one, it’s simple and nice to prepare the presentation. This’s not difference from the previous research, but you will get a method and can bring to apply to Thailand context. It will be useful if it can decrease patient & death ratio in our country. For the future work, this can adapt contact tracking or data analytics to be the factors that can fullfill the outcome in differrence way under GIS-based maps to make the public health decisions about other emerging diseases in Thailand.

For the second one, it’s complex and there are many statistics method, for example, Regression, Stepwise Regression and Spatial Lag Regression. This may take time to prepare and learn, but fianlly you will find the significant association between population and earlier epidemics which’s useful outcome.
Cheer up ka!! 🙂

[imktd8]

For the first paper, it’s interesting in Thailand context and it challenge to collect data from the area that has the highest Dengue Hemorrhagic Fever (DHF) in Thailand. This research use GIS technology and spatial analysis techniques tools to describe epidemiological patterns. It may be concerned in data privacy becuase of data entry to The OSD system by social network (i.e. Facebook) account. (This may risk, ref. case of millions Facebook users’ personal data was harvested without consent by Cambridge Analytica) and security issue (URL http://www.s-cm.co/dengue, the protocal http is not secure. It should use https) For my question, it not have to login by facebook User ID if not share data to social or integrate to Facebook API. For the Evaluation of OSD’s user satisfaction, lack of ease of use factor that may help to imply about user interface and return satisfaction to system that impact to system usage. In my opinion, this system’s not still the advanced analytics follow its research name 100%.

Then I would like to prefer you to select the second paper; “Using spatial analysis and GIS to improve planning and resource allocation in a rural district of Bangladesh” which use GIS maps to support the prioritisation of underserved unions. For the objective of this paper to use GIS technology in public health is common in Bangladesh. I think this can apply to Thailand context and can give the valuable outcome to our country 🙂 Although this research has a limitation of poor GIS capacity, but I believe in Thailand nowsday, we have the hi-technology & technical skill in hi-spedd internet & GIS/ GSP technology that can support and close this issue. Cheer!

[imktd8]

These are great papers Ameen, I like both of them. It’s hard to select just one topic. For the first paper is in COVID-19 context that very interesting today. Currently, the coronavirus disease 2019 (COVID-19) outbreak has spread across the world and has become a pandemic. This paper study about the influence of meteorological factors on the transmission and spread of COVID-19 by examine the associations of daily average temperature (AT) and relative humidity (ARH) with the daily counts of COVID-19 cases in 30 Chinese provinces. As I consider, I think that the objective is clear, but the research method and variable is not different from the previous research. For data collection, it used 14-day to collect data, but as I know there is time period of COVID-19 disease time more than 14-day and it used small sample size in some provinces that leads to model instability and invalid estimates. Finally, only factor & model in this research may not enough. For the accurate result and can adapt to apply a research outcome to the other areas, it should improve the research experiment, stat. model, data collection period & factor to get the new finding in the future.

Yes!! I would like to prefer you to select to the second paper.. ‘Mapping sites of high TB transmission risk: Integrating the shared air and social behaviour of TB cases and adolescents in a South African township” althouhe is noy newly than COVID-19, but TB remains a major public health problem around the world today. The objective of this paper is clear and interesting. To investigate the social behaviour patterns of individuals who have developed TB disease and adolescents at risk of infection by studying in CO2/GIS monitors and location diaries were given to individuals from a South African township. For the population, it set to 3 groups: (1) Newly diagnosed TB patients (2) Recently treated TB patients (3) Adolescents and use the Rudnick-Milton variant of the Wells-Riley TB transmission model. Finally, this research get the new methodology to uncover TB transmission hot spots using a technique that avoids the need to pre-select locations. This may help to decrease to TB transmission and patient in the future.

[imktd8]

We are in the era of Information and communication technology that can be used to improve the quality and safety of health care. In my opinion, although there are some Healthtech startups that do not solve the health problem in the Thailand context. There is an inadequate supply of skilled individuals who have the technical skills to use this technology to improve health care. As Khun Chanapong said that there are less than ten Thai doctors who are also health informaticians and a programmer or software engineer who interested in health informatics is just a small group.

To challenge this issue, It is important to identify and develop the skills, training to a person who is in a related field, for example, the doctor should have IT skills (help to analyze, design and utilize new technology – Phyton, Machine learning, Advance predictive, data analytics etc.) or IT worker should have medical knowledge (help to analyze, realize problem and design a system or software that match with user requirement etc.) An ideal approach will include needs assessment as well as education and training opportunities for that workforce.

[imktd8]

If I have to pay for data set in my country, data sharing is one choice for me to get data that I need. “Data Sharing” is a data exchange process where open, freely available data formats and process patterns are known and standard. Thus, any organization or individual can use any counterparty’s data and metadata.

Although data sharing is given a benefit, it’s still hard to ensure that data generated from the source are collected, curated, managed and shared in a way that maximizes their benefit. On the other hand, data that is shared will be protected or not? Should to have a “Data Sharing Agreement” for data sharing? Fro data sharing, to prepare data with high quality, security protection, legitimate are important factors.

[imktd8]

WHO defines that Universal health coverage (UHC) is a vision of all people obtaining quality health services without suffering financial hardship. UHC means that all individuals and communities receive the health services they need without suffering financial hardship. It includes the full spectrum of essential, quality health services, from health promotion to prevention, treatment, rehabilitation, and palliative care.

Only 58 countries (30.41%) have achieved UHC which involves three coverage dimensions – health services, finance, and population. It is defined as the legislation that provides for universal health insurance and > 90% coverage for skill birth attendance and prepayment health insurance that assures the service coverage with legal guarantees.

From the article “If you want to save money on healthcare, get sick in some other country “. The implementation of UHC in different settings. Many countries, however, remain challenged by financial constraints, increasing citizen demands, political obstacles, the surge in non-communicable diseases on top of the unfinished agenda of infectious, maternal, and child deaths, and by the complexity of moving towards UHC. Like US and South Korea. WHO has identified that poor government stewardship, governance and health delivery system are the main challenges in developing countries. Then Thailand and South Korea are in this scope. In USA, the cost of living index and healthcare in the US is higher than another country. I agree with Khun Chanapong that there are different problems of UHC in different settings to suitably resolve according to their social and economic environment.

[imktd8]

Net Pracharat is a flagship digital infrastructure development project of Thailand. The main objective of Net Pracharat is to strengthen National Broadband Network by expanding high-speed Internet networks to reach all villages in the country and the local Thai people who live in remote areas will be able to access broadband or high-speed Internet.

With Net Pracharat, local people can access useful information and services in many areas, such as education, public health, and government services – leading to improvement in the quality of life.

For the profit that the Thai people will get from this service is a lot, but there are cons too. Below are cons that I think that they will expend to a big problem in one day in the future:
– Security and data privacy problems.
– Overload demand in internet speed and time period for usage.
– Cost in maintenance and upgrade infrastructure.
– Data phishing (rural user lack of knowledge and method to prevent phishing attacks)
– The social problem, for example, youth or student use internet in the wrong way (playing game /gambling online, watching pornography)
etc.

[imktd8]

For the disease that I am interested to apply maths modeling is Influenza, as Thailand is a tropical country. This disease can rapidly spread to all genders and ages. It can spread easily from humans to humans through the air from coughing, sneezing etc likes COVID-19. Furthermore, influenza has affected people in Thailand every year. In WHO influenza update of 28 Sep 2020, influenza detections were reported in Thailand. Although there are prevention vaccinations available, people who have received vaccines can still be infected with influenza because vaccines are only 40-60 per cent effective based on the strain of influenza causing the outbreak each year and this makes me hope that maths modeling may suggest to way to reduce the outbreak or planning to prevent this disease in Thailand context.

[imktd8]

Your dashboard has a nice theme and easy to understand. It’s nice to have a time period or ranking by country or continent. For the color of each country, it should display in the different color.

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