Week 1: Discussion on choice of disease and questions

[Wirichada Pan-ngum]

By the end of the week, I would like you to submit to the discussion board.

The disease topic you are interested to explore as your individual assignment – choose one that you are familiar with and clarify the scope of your research e.g. is it at a region/country/ global level?

What research questions you want to use the modelling approach to gain better understanding? – could be assessing impacts of interventions, estimating key parameters, better understanding the transmission or impacts of external factors on transmission.

Your friend and I will make some comments to help shape up the research project. (10 points)

Note: I think it might help to search on Pubmed or google using the keywords such as mathematical modelling, transmission dynamics, XXX (disease), XXX (interventions interested) for example.

[Thundon Ngamprasertchai]

Topic: A 13-valent pneumococcal vaccine (Prevnar 13®) implementation in Thai elderly
Invasive pneumococcal disease in elderly is preventable by vaccine in high income countries. However, this vaccine is not provided by Thai national immunization program according to high cost.

Rationale: Previous studies on economic evaluation of pneumococcal vaccine in Thailand based on static rather than infectious modeling approach.

Research question: Is it worth to implement a 13-valent pneumococcal vaccine in Thai societal perspectives based on infectious modelling approach?

References:
– Kulpeng W, Leelahavarong P, Rattanavipapong W, et al. Cost-utility analysis of 10- and 13-valent pneumococcal conjugate vaccines: protection at what price in the Thai context? Vaccine. 2013;31(26):2839-47.
– Dilokthornsakul P, Kengkla K, Saokaew S, et al. An updated cost-effectiveness analysis of pneumococcal conjugate vaccine among children in Thailand. Vaccine. 2019;37(32):4551-60.
– Ounsirithupsakul T, Dilokthornsakul P, Kongpakwattana K, Ademi Z, Liew D, Chaiyakunapruk N. Estimating the Productivity Burden of Pediatric Pneumococcal Disease in Thailand. Appl Health Econ Health Policy. 2020;18(4):579-87.

[Wirichada Pan-ngum]

Thank you for the topic. Please study the prevalence of infections among age group? how is it transmitted? How the vaccine work (reduce severity, block transmission,..)? Please look for one or two models published if there are any, what settings were they?

[Kridsada Sirichaisit]

Topic : Evaluating the effectiveness of the MDR surveillance system in Buengkan hospital

Rationale : Multidrug-resistant bacteria (MDR) are major causes of nosocomial infections and are associated with high morbidity, and mortality. The nosocomial infection surveillance system is the intervention to reduce the MDR problem. In Buengkan hospital has the real time MDR surveillance system and I want to determine the effectiveness of this surveillance system by using mathematical modeling.

Research question: How much of an effect of the MDR surveillance system in the reduction of the incidence rate of MDR bacteria in hospital?

Reference

Karl G. Kristinsson, Mathematical Models as Tools for Evaluating the Effectiveness of Interventions: A Comment on Levin, Clinical Infectious Diseases, Volume 33, Issue Supplement_3, September 2001, Pages S174–S179, https://doi.org/10.1086/321845

Tahir H, López-Cortés LE, Kola A, Yahav D, Karch A, Xia H, et al. (2021) Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals – a mathematical modeling study. PLoS Comput Biol 17(2): e1008600. https://doi.org/10.1371/journal.pcbi.1008600

[Wirichada Pan-ngum]

Great topic. The modelling paper in Plos Computational is a good one to read. I am not sure why it is solely assumed transmission come from patients’ movement, and not hospital staff??

[Kridsada Sirichaisit]

In my working hospital, The MDR bacteria frequently transfer from the ICU patients to the other ward because the ICU is the high prevalence of MDR bacteria and the Enterobacteriacae such as E. coli can colonization into the GI tract of the patients. If the destination not working properly in the infection control protocol, the medical staff will transfer the MDR bacteria to the other patients.

[Wachirawit Supasa]

Analysis of Dengue Fever pattern from seasonal climate setting in Mae Hong Son

Rationale: Dengue Fever or DF is an important high-mortality disease caused by the Dengue virus which can transmit between mosquitoes of the genus Aedes and humans. Aedes mosquito has a distinct life cycle as it required a specific temperature, humidity, and rainfall range to develop from egg to adult. The climate of Mae Hong Son is drastically different in each season where temperature can vary from 8.2 Celcius in winter to 41.5 in summer and the Aedes mosquitoes depend on these factors for their development which results in the seasonal pattern of Dengue Fever endemic. Mae Hong Son is also the highest DF patient per capita in Thailand.

Research question: Can climate patterns predict Dengue Fever endemic in Mae Hong Son?

References:
1. Bartley LM, Donnelly CA, Garnett GP. The seasonal pattern of dengue in endemic areas: mathematical models of mechanisms. Trans R Soc Trop Med Hyg. 2002 Jul-Aug;96(4):387-97. doi: 10.1016/s0035-9203(02)90371-8. PMID: 12497975.
2. Chanprasopchai P, Pongsumpun P, Tang IM. Effect of Rainfall for the Dynamical Transmission Model of the Dengue Disease in Thailand. Comput Math Methods Med. 2017;2017:2541862. doi: 10.1155/2017/2541862. Epub 2017 Aug 8. PMID: 28928793; PMCID: PMC5591907.
3. Chanprasopchai P, Pongsumpun P, Tang IM. Effect of Rainfall for the Dynamical Transmission Model of the Dengue Disease in Thailand. Comput Math Methods Med. 2017;2017:2541862. doi: 10.1155/2017/2541862. Epub 2017 Aug 8. PMID: 28928793; PMCID: PMC5591907.

[Anawat ratchatorn]

Very interesting question and also good point about climate pattern.
I just know that highest DF patient area in Thailand is Mae Hong Son, so this research question might be very helpful.

[Wirichada Pan-ngum]

If we believe that climate factors play important role in this we should see if the climate patterns there are really different from other places in Thailand. There are probably many time series (stat models) that try to look at the association between climate factors and mosquito/egg population. In mechanistic model (maths model) we will try to explain that possible relationships in maths equation form. What is it that the climate do to the mosquito? more eggs, live longer, bite more, etc.

[Anawat ratchatorn]

Topic <- Analysis of Leptospirosis in individual diagnosed with metabolic syndrome

Rational <- Leptospirosis was first appear in Thailand in 1942 (1) then the infection was increasing significantly in 1996 (2) and now still have an average annual incidence rate of 6.6 per 100,000 population (1). In Thailand, most affected area are Northeastern and Southern Regions (1) and highly associated with rainy season and flooding (3)(4) . Individual factors related to Leptospirosis infection are age, sex, occupation and behavior (4) and most cost-effective strategy for the control of leptospirosis is the combination of the vaccination and treatment of infective livestocks (5). Metabolic syndrome has negative impact on immunity and chronic disease progression (6) and the prevalence in Thailand, in 2011, was about 24 – 32.6% (7). If we can analysis the relation between metabolic syndrome patient and Leptospirosis infection, it could make us better understanding about risk factors of getting infection and help us to prevent people in Thailand from Leptospirosis infection more effectively.

Research question – How does metabolic syndrome affect risk of Leptospirosis.

References <-
(1) Narkkul, U., Thaipadungpanit, J., Srisawat, N. et al. Human, animal, water source interactions and leptospirosis in Thailand. Sci Rep 11, 3215 (2021). https://doi.org/10.1038/s41598-021-82290-5
(2) Tangkanakul W, Smits HL, Jatanasen S, Ashford DA. Leptospirosis: an emerging health problem in Thailand. Southeast Asian J Trop Med Public Health. 2005 Mar;36(2):281-8. PMID: 15916031.
(3) Chadsuthi, S., Chalvet-Monfray, K., Wiratsudakul, A. et al. The effects of flooding and weather conditions on leptospirosis transmission in Thailand. Sci Rep 11, 1486 (2021). https://doi.org/10.1038/s41598-020-79546-x
(4) Jaruwan Viroj. Public Health Prevention and Spatiotemporal Analysis of Human Leptospirosis in
Mahasarakham Province, Thailand. Human health and pathology. Université Montpellier, 2019.
English. ffNNT : 2019MONTG028ff. tel-02468153
(5) Okosun, Kazeem Oare, Mukamuri, M. and Makinde, Daniel Oluwole. “Global stability analysis and control of leptospirosis: ” Open Mathematics, vol. 14, no. 1, 2016, pp. 567-585. https://doi.org/10.1515/math-2016-0053
(6) Andersen CJ, Murphy KE, Fernandez ML. Impact of Obesity and Metabolic Syndrome on Immunity. Adv Nutr. 2016 Jan 15;7(1):66-75. doi: 10.3945/an.115.010207. PMID: 26773015; PMCID: PMC4717890.
(7) Aekplakorn W, Chongsuvivatwong V, Tatsanavivat P, Suriyawongpaisal P. Prevalence of metabolic syndrome defined by the International Diabetes Federation and National Cholesterol Education Program criteria among Thai adults. Asia Pac J Public Health. 2011 Sep;23(5):792-800. doi: 10.1177/1010539511424482. PMID: 21984495.

[Wirichada Pan-ngum]

Similarly to the dengue climate model, we need to think in term of the mechanistic of the system. What does the metabolic syndrome do to leptospirosis infection. Do they increase susceptibility to the infection? reduce severity of infection? My previous work on Melioidosis modelling mentioned bits of DM in increasing the severity of infection in melioidosis

Mahikul W, White LJ, Poovorawan K, Soonthornworasiri N, Sukontamarn P, Chanthavilay P, Medley GF, Pan-Ngum W. Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis. PLoS Negl Trop Dis. 2019 May 9;13(5):e0007380. doi: 10.1371/journal.pntd.0007380. PMID: 31071094; PMCID: PMC6529009.

[Anawat ratchatorn]

Thank you very much for your kind response.
I would like to ask you some question that I don’t clearly understand about doing a research ,due to my less experience of how to do a research.

I wonder that if we already know about metabolic syndrome reducing individual immunity.

Can we use this statement to create a research about relationships between any infectious diseases and the effect of metabolic syndrome (based on a knowledge which we know it reduces immunity) ?

OR we have to have stronger evidence which indicate the effect of metabolic syndrome can worsen specific disease before we do a mathematical model about the specific disease and metabolic syndrome ?

Respectively thank you in advance.

[Wirichada Pan-ngum]

Model can be used to test the hypothesis here. So you don’t need the strong evidence before conducting a model and if you already have the strong evidence then may be you would not need a model…data clearly indicates that. Then we can test that hypothesis by showing that the model can fit the observed data that is available. Let’s say if the model cannot fit the data, then we can ask further questions about the model or other hypotheses. The usefulness of this model is to help further predict what may happen if we believe we have a good model.

[Mingkhwan Vithayaveroj]

Topic:
Cost-effectiveness of COVID-19 lockdown on nation economy: difference-in-differences method.

Rationale:
In March-April 2020, both UK and Thailand have many similar situations in the COVID-19 pandemic, such as the trend in the proportion of new case infectious/population. They also have nation lockdown but are different in detail. The United Kingdom forces all-day lockdown, except for some necessary activities. At the same time, Thailand closed some dangerous places but ordered the prevention to leave the residence just from 10 pm to 4 am. This difference leads to the question: Are differences in lockdown between The United Kingdom and Thailand cost-effective, both on COVID-19 prevention and economy?

Research Question:
Are differences in lockdown between The United Kingdom and Thailand cost-effective, both on COVID-19 prevention and economy?

Rough Framework:
Rough Framework

References:
#Difference-in-Differences Method
Goodman-Bacon and Marcus (2020). Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies.

#Covid prevention and Economy
Gathergood and Guttman-Kenney (2021). The English Patient: Evaluating Local Lockdowns Using Real-Time COVID-19 & Consumption Data.

Allen (2021). Covid Lockdown Cost/Benefits: A Critical Assessment of the Literature.

Zhang et al. (2021) Modeling coupling dynamics between the transmission, intervention of COVID-19 and economic development.

[Wirichada Pan-ngum]

Nice framework for start. You can model different level of lockdown and its impacts on health outcomes. There are costs associated with both the interventions and the health outcomes. So we can compare the relative values between UK and Thailand. Think more about data related to this work.

[Theekhathat Huapai]

Topic : Implement a fee schedule cardiovascular risk screening in the first-degree relatives of stroke patients in regional health 3 — a cost-effectiveness study.

Rationale : Stroke is the leading cause of disability and mortality in regional heath 3. The etiology of stroke is non-modifiable risks such as age, gender, genetics, and modifiable risk such as hypertension, smoking, diabetes mellitus, high blood cholesterol. The current cardiovascular risk screening guideline by the national health security office (NHSO) focuses on hypertension and diabetes mellitus patients. Many studies indicated that first-degree relatives of stroke patients have a higher risk of stroke than the general population.

Research question : The national health security office had announced that a person above 35 years old could take cardiovascular risk screening. There is still no cost-effectiveness study to implement as a fee schedule program. Screening cardiovascular risk in the first-degree relatives of stroke may be a model of the fee schedule program.

[Wirichada Pan-ngum]

I have done something similar with DM screening model. May be useful to read and think about the data.

Mahikul W, White LJ, Poovorawan K, et al. A Population Dynamic Model to Assess the Diabetes Screening and Reporting Programs and Project the Burden of Undiagnosed Diabetes in Thailand. Int J Environ Res Public Health. 2019;16(12):2207. Published 2019 Jun 21. doi:10.3390/ijerph16122207

[Naphat]

Topic; Estimating asymptomatic and undetected infections of COVID-19 in Thailand

Rational; The outbreak of epidermic of Coronavirus disease 2019 (COVID-19) has led to more than 1.3 million total reported infections cases and about 13k deaths in Thailand. The increasing number of cases affects the public health resources of Thailand is insufficient due to many factors such as difficult access to COVID testing, expensive testing costs and etc. Another important factor in the epidemic is that asymptomatic and undetected individuals keep the epidemic and the rate of infection increasing.

Research question; Will this model be able to estimate the number of asymptomatic and undetectable cases and predict the number of future cases in order to reduce the impact of resource shortages on Thai public health?

[Wirichada Pan-ngum]

Covid modelling is an interesting one. Think about what you would need to take into account there. All interventions that have been happening is one thing. A lot of literature on this topic, so keep reading and discuss together later.

[Khaing Zin Zin Htwe]

Topic:
Effect of coup d’etat on drug-resistance tuberculosis (DR-TB) morbidity and mortality

Rationale:
Myanmar is one of DR-TB high burden countries, and the control measures were implemented by the National TB control programme together with non-governmental partners. As a result of coup d’etat and subsequent health system breakdown nationwide, there have been disruption in both early diagnosis and treatment measures and adherence monitoring. It is needed to measure how large the negative impact of coup d’etat would be on DR-TB morbidity and mortality so that effective health planning can be made to cover the aftermath.

Research question:
Can this model predict the effect of coup d’etat on DR-TB morbidity and mortality in Myanmar?

Reference:
High rates of multidrug-resistant and rifampicin-resistant tuberculosis among re-treatment cases: where do they come from?

Note:
Since this topic is politically related and there might be a huge variety of contributing factors, I have no idea if this question might be answerable. Thank you in advance for comments and recommendations.

[Navinee Kruahong]

This is an interesting topic and very important for public health. Nice choice! I have read your research question and just wonder that do they already have a model on this topic? and then you wanna test how the model fit in your conditions?

[Khaing Zin Zin Htwe]

Dear, thank you for your encouragement. I’m sorry for the confusing research question. What I meant with “this model” is the model which I would be working on later in the project, not a pre-existing model. Yeah, it looks confusing even to me now 😀
Thank you again for pointing out. I think it’d be better to update the research question to “How can coup d’etat affect DR-TB morbidity and mortality in Myanmar?”.

[Wirichada Pan-ngum]

So in your TB model you need to have compartments of diagnosis going into treatment and also accounting for adherence. These things get affected from the unpeaceful event in some ways. Then you need data before and after the event to prove the model and estimate some parameters. Interesting. Keep working on that.

[Navinee Kruahong]

Topic: Suicide

Rationale:
Suicide and suicide attempts constitute major public and mental health problems in many countries. The risk factors of suicide include not only psychological and other individual features but also the characteristics of the community in which the people live. Therefore, in order to better understand the potential impacts of community characteristics on suicide, the regional level effects of suicide need to be thoroughly examined.

Research question:
How does reginal risk factors and discern spatial patterns in suicide risks affect suicide rates in each region in Thailand? and Can these risk factors predict reginal suicide rates?

Reference:
Phillips, J. (2013). Factors Associated With Temporal and Spatial Patterns in Suicide Rates Across U.S. States, 1976-2000. Demography, 50(2), 591-614. Retrieved September 7, 2021, from http://www.jstor.org/stable/42920539

Congdon P. The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality. Urban Stud. 2011;48(10):2101-22. doi: 10.1177/0042098010380961. PMID: 22069804.

[Anawat ratchatorn]

Very interesting topic.
Suicide, nowadays, becomes more serious problem in our society.
To understand the factors related to this problem will be very helpful.

[Wirichada Pan-ngum]

It sounds more like a spatial/statistical modelling to me unless committing suicide is infectious or affecting others who are in close contacts with the person. I suggest reviewing published work on this topic to see if anyone has used mathematical modelling to explore this before and how. Interesting!

[Anawat ratchatorn]

Edited – My apologies, I reply for wrong post.

[Wirichada Pan-ngum]

Thanks for many great ideas so far. Well done! Please keep moving on to learn more about modelling and trying to link it back in to your idea of project while studying the materials.

[Weerada Trongtranonth]

Topic: Covid-19

Rationale: Since the cover-19 pandemic is cause of death, We have to find out the way to prevent the outbreak situation as much as we can. To know the correlation of the outbreak would be pros for prevention

Research question: Correlation between climates indicator and Covid-19 in Thailand

References: Anderson, R.M., Heesterbeek, H., Klinkenberg, D., Hollingsworth, T.D., 2020. How will
country-based mitigation measures influence the course of the COVID-19 epidemic?
Lancet 395, 931–934.
Bull, G., 1980. The weather and deaths from pneumonia. Lancet 315, 1405–1408

[Wirichada Pan-ngum]

@Weerada, sound like a nice COVID model but choose one between climate factors or mitigation measures I think. Both can be complicated for a short term project here.

[Wirichada Pan-ngum]

Cool. We are also working on Rabies problem. Not so much on the oral vaccine but just on the dog population dynamics and the impact of dog sterilization and control of rabies. Looking forward to hearing more on this.

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