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.

[Zarni Lynn Kyaw]

Disease topic: Hepatitis
Scope of research: South East Myanmar (Karen)

Research question: What are the key factors that contribute to the transmission of hepatitis C in Myanmar?

Some keywords that I could use to search for relevant literature:

1) Mathematical modeling of hepatitis in Myanmar
2) Transmission dynamics of hepatitis in Myanmar
3) Hepatitis C treatment in Myanmar
4) Economic impact of hepatitis in Myanmar

After a quick search using those keywords in PubMed, there are very little research done using mathematical modeling in Myanmar but there are more than 40 articles on Hepatitis treatment.

I found one specific paper which relate to my research question. Hepatitis C elimination in Myanmar: Modelling the impact, cost, cost-effectiveness and economic benefits

Having done a preliminary paper search I believe that there are 4 main domains in the knowledge gaps that I can help fill, if I were to conduct a mathematical modeling paper on hepatitis in south east Myanmar.

Assessing the impact of interventions:
I could use mathematical modeling to assess the impact of different interventions, such as vaccination programs, treatment programs, and public awareness campaigns. I could model the spread of hepatitis C in Myanmar under different intervention scenarios, to see how the number of new infections would be affected.

Estimating key parameters:
I could use mathematical modeling to estimate key parameters, such as the transmission rate of hepatitis or the effectiveness of various intervention. This information could be used to improve the design and implementation of interventions.

Better understanding the transmission:
I could use mathematical modeling to better understand the transmission dynamics of hepatitis. I could model how the virus is transmitted through different routes, such as blood, sexual contact, or mother-to-child transmission.

Impacts of external factors on transmission:
I could use mathematical modeling to study the impact of external factors on the transmission of hepatitis. I could model how the spread of hepatitis would be affected by changes in the population, such as the number of people who are infected with HIV.

[Wirichada Pan-ngum]

The four areas of work you mentioned towards the end are all valid. The next step would be to look for the right structure of the disease progression-transmission (i.e. SIR, SEIR etc). Continue literature review to see what would be the questions suitable for Myanmar setting, how interventions are in place and what may be the issue in the setting. Remember that we should start simple always, thus there would be some assumptions behinds that we keep to make the model simple enough…note those down if you like and we can discuss later.

[Zarni Lynn Kyaw]

Dear Arjan,

Thanks for the suggestion, I’m reading up on the literature to get a better understanding as well. Although I have a simple research question “What are the key factors that contribute to the transmission of hepatitis C in Myanmar?” it can be updated if the existing literature point me in a different direction or I found there is a research gap.

I will also keep in mind to make the model simple but I think SEIR model is suitable in this case. I’m also happy to discuss it with you as well.

Thanks,
Zarni

[Wirichada Pan-ngum]

Good job.

[Tabitha Okech]

Choice of disease: Malaria
Research question: What is the cost-utility and the budget impact of scaling up RTS, S/AS01 malaria vaccination of children across different malaria transmission settings relative to scaling up other malaria interventions in Kenya

Study population: Malaria endemic regions in Kenya

Rationale/significance of the research:
There have been a lot of efforts in malaria control from all fronts. These include interventions such as insecticide-treated nets (ITN), indoor residual spraying (IRS), larviciding, chemoprevention, and intermittent preventive treatment (IPT) of pregnant women and infants. Despite all the efforts and evidence of a decrease in malaria cases, malaria is still endemic in sub-Saharan Africa. The progress on elimination is slowing and innovations are needed (Sauboin et al, Kaslow et al). To accelerate this goal, there have to be increased innovations for public health interventions that reduce mortality and morbidity (Alonso et al). Immunization is one of these preventive interventions, with both health and economic benefits to a country (Fullman et al). So far, only one malaria vaccine candidate- RTS, S/AS01 has passed the Phase III trial stage and has been approved for widespread use. RTS, S is a circumsporozoite protein (CSP) subunit vaccine that has been under development for the last three decades by GlaxoSmithKline (GSK) in collaboration with Walter Reed Army Institute of Research (WRAIR) since 1984. R represents the central repeat region, T is the T-lymphocyte epitopes, and S is the surface portion of the Hepatitis B antigen that acts as the carrier matrix for the central repeat region. The other S represents the unfused portion of the Hepatitis B surface antigen that fuses to the RTS. AS01 is the adjuvant portion that improves the RTS, S immunogenicity (Laurens et al). Despite its considerably modest efficacy (Partnerships SCT), it received a positive opinion from the European Medicines Agency following a scientific evaluation by the Committee for Medicinal Products for Human Use in 2015. Subsequently, in 2015 RTS, S/AS01 malaria vaccine got WHO clearance for pilot implementation in three countries in Sub-Saharan Africa- Ghana, Kenya, and Malawi. The implementation in these countries started in 2019 (WHO). In October 2021, WHO recommended widespread use of the RTS, S/AS01 malaria vaccine to prevent Plasmodium falciparum malaria in children living in regions with moderate to high malaria transmission intensity

RTS, S/AS01 malaria vaccine has been ongoing in three countries in Sub-Saharan Africa since 2019 (18). WHO’s recommendation for large-scale implementation of RTS, S mostly focuses on uncertainties around the feasibility of delivering a four-dose schedule that includes new immunization visits in LMIC with already existing immunization coverage challenges (WHO, Asante et al). Additionally, the possibility of a rapidly decreasing protection remains a major disadvantage of this vaccine, coupled with safety concerns of unexplained excess meningitis cases and greater sex differences in all-cause mortality in girls. Countries have considered scaling up malaria treatment and vector control programs as more cost-effective than introducing the malaria vaccine at the current status quos (Mahmoudi et al).

Healthcare systems policy and decision-makers are increasingly adopting results from economic evaluations, such as cost-effectiveness analysis (CEA) and economic impact of health products and technologies including vaccines to support decisions on allocation of healthcare resources (Sara et al). Economic evaluations of RTS,S/AS01 malaria vaccine are still limited in resource-constrained countries- low-income countries (LICs) and lower middle-income countries (LMICs) owing to a lack of both country-specific cost data, clinical effectiveness data and the research capacity (Galactionova et al; Marie et al).
Given that the cost-effectiveness of malaria vaccination varies by different factors such as malaria transmission intensity and country income level, there is a need for country-specific evidence to ensure the efficient allocation of funding toward optimal implementation strategies for malaria vaccination especially when budget is constrained. I aim to conduct a CUA of the malaria vaccine and determine the budget impact of the malaria vaccine scale-up in Kenya.

Modeling can play a crucial role in answering various research questions related to developing and deploying the RTS/ S/AS01 malaria vaccine. These may include:

1. Efficacy Assessment: What is the expected efficacy of a malaria vaccine candidate under different scenarios (e.g., different transmission intensities, population demographics, and vaccine coverage levels)?

2. Impact on Transmission: How will the introduction of a malaria vaccine affect the transmission dynamics of the disease in endemic regions? What is the potential for herd immunity?

3. Optimal Deployment Strategies: What are the most cost-effective and efficient strategies for deploying a malaria vaccine in terms of target populations, timing, and coverage levels?

4. Long-Term Durability: What is the expected duration of protection provided by the malaria vaccine, and how does this impact the overall effectiveness of vaccination programs?

5. Impact on Severe Disease and Mortality: How does vaccination impact the incidence of severe malaria cases and malaria-related deaths? Can modeling estimate the potential reduction in mortality?

6. Integration with Other Interventions: How should malaria vaccination be integrated with other malaria control interventions, such as bed nets, insecticide spraying, or antimalarial drug distribution, to maximize its impact?

Modeling will help to address these questions by simulating different scenarios, estimating outcomes, and optimizing vaccination strategies to inform decision-makers and stakeholders in the fight against malaria in Kenya.

[Hazem Abouelfetouh]

Choice of Disease and Scope:
Hepatitis C virus (HCV) in Egypt

Rationale/significance of the research:
Hepatitis C virus (HCV) is one of the leading known causes of liver disease in the world. Viral hepatitis has been estimated as the 7th leading cause of mortality globally. About half of this mortality is attributed to hepatitis C virus (HCV), a virus that causes acute hepatitis, fibrosis, cirrhosis, and liver cancer among other disease sequelae.

Egypt has one of the highest prevalence rates of hepatitis C in the world, with 15% of the adult population infected. In 2018, 6% of individuals aged 1–59 years had a positive result on the hepatitis C antibody test, and 4% were found to have an active infection. HCV is one of the significant public health concerns in Egypt that needs more attention and funding from health policymakers. Indeed, Liver disease is the main cause of mortality in Egypt.

Hepatitis C has complex transmission patterns in Egypt and is characterized by unique patterns, including iatrogenic transmission through medical procedures and possible social and cultural factors. Mathematical modeling can help disentangle these complex transmission dynamics and inform targeted interventions.

Egypt faces resource constraints in its healthcare system. Mathematical modeling can assist policymakers in allocating limited resources more effectively by optimizing various intervention strategies.

State the research questions where modeling is likely to help answer:
What are the most effective strategies for reducing the burden of hepatitis C in a high-prevalence region, such as Egypt, and what are the key drivers of hepatitis C transmission and how can we optimize the allocation of healthcare resources in Egypt?

Citations on other previous work that you may use to guide your study (if there are any):
– Current situation of viral hepatitis in Egypt
– Impact of treatment on hepatitis C virus transmission and incidence in Egypt: A case for treatment as prevention
– Estimation of hepatitis C virus infections resulting from vertical transmission in Egypt
– Effect of preventive and curative interventions on hepatitis C virus transmission in Egypt (ANRS 1211): a modelling study
– Estimation of hepatitis C virus infections resulting from vertical transmission in Egypt

[Panyada Cholsakhon]

– The disease topic: Dengue Fever

– Scope of the research: Country level, Thailand

– Research Question: What are the specific climate factors such as temperature, humidity and rainfall that influence the transmission dynamic of Dengue fever in Thailand.

– Rationale: Per the WHO, Dengue as one of the top ten threats to public health and the disease is associated with significant societal and economic burdens.The Incidence of dengue virus has soared in 2023 to near historically high levels, reported by WHO, more than 5 million cases worldwide and 5,000 deaths from the virus that still lacks an effective treatment or vaccine.

In South and Southeast Asia there is a significant rise in dengue cases, with indications that global warming may contribute to a potential record-breaking number of infections worldwide. In Malaysia, for instance, reported a staggering surge in dengue cases, recording 56,721 cases up to July 20,2023 compared to 23,183 cases during the same period the previous year—an alarming increase of 144.7% while the number of deaths more than doubled compared to 2022, reaching 39. Similarly, Thailand’s Department of Disease Control noted a sharp increase in dengue cases, with 46,855 cases and 41 fatalities registered as of July 19,2023. This marks a significant rise from the 16,542 cases reported throughout the entirety of the previous year, representing the highest rate since 2020. The surge in infections has also been observed in other countries such as Cambodia, the Philippines, and Sri Lanka.

Given the high burden of dengue with the absence of effective vaccine, the primary method to prevent dengue transmission remains vector control. Mathematical models have long been used to describe the dengue transmission and it serves as a guiding tool for decision-making. Investigating the climate factors influencing the dynamic transmission of Dengue fever can contribute to public health authorities’ capacity for implementing intervention measures for disease control.

* References:
– https://healthpolicy-watch.news/dengue-cases-approach-historic-highs-local-transmission-seen-in-europe/
– https://www.benarnews.org/english/news/bengali/dengue-asia-record-infections-08072023151754.ht

*Citations on other previous work that you may use to guide your study (if there are any):

– Aguiar, M., Anam, V., Blyuss, K. B., Estadilla, C. D. S., Guerrero, B. V., Knopoff, D., Kooi, B. W., Srivastav, A. K., Steindorf, V., & Stollenwerk, N. (2022, Mar). Mathematical models for dengue fever epidemiology: A 10-year systematic review. Phys Life Rev, 40, 65-92. https://doi.org/10.1016/j.plrev.2022.02.001

– Chanprasopchai, P., Pongsumpun, P., & Tang, I. M. (2017). Effect of Rainfall for the Dynamical Transmission Model of the Dengue Disease in Thailand. Comput Math Methods Med, 2017, 2541862. https://doi.org/10.1155/2017/2541862

– Naish, S., Dale, P., Mackenzie, J. S., McBride, J., Mengersen, K., & Tong, S. (2014, 2014/03/26). Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC Infectious Diseases, 14(1), 167. https://doi.org/10.1186/1471-2334-14-167

[ABDILLAH FARKHAN]

The choice of disease I would like to explore using mathematical modeling is the Monkeypox outbreak in Indonesia, an originally zoonotic infection transmitted from wild animals to humans but now occurring from humans to humans. Mpox has historically been a neglected zoonotic disease which mathematical models to explain its transmission dynamics are limited.

In Indonesia, the first confirmed mpox disease was reported in August 2022, involving a 27-year-old man with prior overseas travel. One year later on October 2023, Indonesia reported the second mpox case and started to massively conduct active case detection. By the end of 2023, the total confirmed cases had reached 72 cases distributed throughout the 6 provinces where 98.6% of cases were concentrated in Java, the most populous island.

Epidemiological investigation revealed an incubation period for mpox ranging from 1 to 21 days, with an average of 7 days. Among 72 cases, only one was female, identified as a spouse of a confirmed male mpox patient. Regarding sexual orientation, the outbreak was reported among 62,5% homosexual individuals, 23% bisexual individuals, and 10% heterosexual individuals. By 2023, mpox became comorbid among 73.6% of HIV patients. Clinical manifestation among all cases exhibited skin lesions and more than half of cases presented 6-25 skin lesions on the face.

Although mpox is considered mild, it is a self-limiting disease whose treatment and prevention control efforts indicate good progress. By the close of 2023, 63 patients had successfully recovered and only one fatality was reported, and the remaining cases were still under isolation. The Indonesia Ministry of Health executed a rapid risk assessment of mpox which the high-risk group targeting HIV patients and homosexual individuals in Jakarta. Following the case resurgence, the government has undergone two doses mpox vaccination with a primary emphasis on men who have sex with men (MSM) and laboratory officers who manage the specimens.

From the rationale above, research questions that may help us to understand the mpox situation using mathematical model are how the mechanism of transmission dynamics in Indonesia and how intervention methods such as two doses of vaccination can help reduce transmission in a high-risk group?

References:
https://infeksiemerging.kemkes.go.id/category/situasi-mpox

Citation:
Peter, O.J., Kumar, S., Kumari, N. et al. Transmission dynamics of Monkeypox virus: a mathematical modelling approach. Model. Earth Syst. Environ. 8, 3423–3434 (2022). https://doi.org/10.1007/s40808-021-01313-2

Molla J, Sekkak I, Mundo Ortiz A, Moyles I, Nasri B. Mathematical modeling of mpox: A scoping review. One Health. 2023 Jun;16:100540. doi: 10.1016/j.onehlt.2023.100540. Epub 2023 Apr 17. PMID: 37138928; PMCID: PMC10108573.

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