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From my topic of interest on Tomato flu, as the limitation of information available on this disease, I instead do the research for effective intervention on HFMD, the origin of Tomato flu. There are two candidate interventions in concern which are vaccination and Educational communication.
For vaccination, though the conservational vaccination for preventing HFMD, FI-EV-A71, is commonly used in several regions, There is a study report(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4685682/) on the inefficiency of the vaccination on CA16, a major cause of Tomato flu. Besides, Some of the research had reported the development of alternative vaccines using β-propiolactone (BPL) to inactivate coxsackievirus
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4130287/), the vaccine is still under development and needs much more information to confirm its efficiency. Thus, this preventive measure is considered unusable due to the lack of evidence and information.As the disease reported is self-limited and no fatal case reported. Educational communication is the only choice left which not most effective but should change something on the outcome. There are several reports have reported that Educational communication plays an important role to prevent HFMD(http://www.jmatonline.com/index.php/jmat/article/view/10816, https://www.sciencedirect.com/science/article/pii/S2213398422000690 ) as it could increase awareness of the exposure group. However, to be a successful intervention, educational communication relies on several factors such as educators (public health staff), the audience(exposure-related group), and the use of that information. The coverage of the intervention also relies on the information that the messenger delivers to the audience.
From this, I will include the perception of the preventive acts on all populations in simulation such as the isolation after being infected. Accordingly, the compartmental model may be altered by adding the Exposed population to the model result which is resulted in the SEIR model.
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Hi Professor, since the objective of my modeling is just to update the paper with the transmission projection, I think the introduction of an intervention/s (eg, PrEP, counseling, barrier methods) would be outside the scope of the project.
However, for the purposes of the discussion, I am considering PrEP as the primary intervention to reduce HIV transmission among genders. It is easier to estimate PrEP usage since it is dispensed on HIV clinics and is controlled by the government. There are also trials indicating the efficacy of these prophylaxis therapy. On an SI model, this intervention interrupts the conversion of a susceptible individual to being infected – assuming that all susceptible individuals adhere to the therapy. Challenges persists on medication adherence such as supply constraints, stigma of going to hygiene clinics, stigma of taking medications for HIV prevention.
Reference: Consolidated HIV guidelines for prevention, treatment, service delivery & monitoring
https://www.who.int/publications/i/item/9789240031593 -
Since my topic of interest is focusing the effect of COVID-19 vaccine along with pneumococcal vaccine for COVID-19 and bacterial pneumonia coinfection. The intervention of this model should be the allocation of those vaccines to the susceptible person especially in children and elderly. Vaccination is the most effective method for preventing pneumococcal disease (World Health Organization, 2019).
Also, the World Health Organization (WHO) recommends that adults receive the pneumococcal conjugate (PVC) vaccine and the influenza vaccine to boost immunity following a COVID-19 vaccination program, although at least 2–4 weeks should pass before receiving these additional vaccines. Therefore, adults and children—especially those with underlying health conditions—the elderly, and those with weakened immune systems should receive all vaccinations as scheduled. Adult vaccines, such as the pneumococcal vaccine and influenza vaccine, are highly recommended to prevent disease and to reduce the likelihood of developing serious symptoms. Annual outbreaks of the novel coronavirus (COVID-19) or influenza cause damage to the tissue of the respiratory system, which weakens it and places it at greater risk of complications arising from bacterial infections. Up to 59.5% of COVID-19 infections in China have been found to involve complications associated with pneumococcal infections (from a study of 257 COVID-19 patients in Jiangsu province carried out between 22 January to 2 February 2020).
However, as part of a nationwide effort to address vaccine-preventable disease, Thailand initiated the National Expanded Programs of Immunization (NEPI), PCVs are not included in the NEPI as of July 2020 (Gamil et al, 2021). If PCVs could be included in the NEPI, it would probably benefit Thai public health. These decisions require that policymakers weigh the costs of vaccination against the incidence of disease in addition to other factors, including the efficacy and safety of vaccines, public health priorities, and logistic issues (Pooripussarakul et al., 2016).
Reference:
– WHO(European Region). Guidance on routine immunization services during COVID-19 pandemic in the WHO European Region, 2020.
– นพ.ทรงภูมิ อธิภูกนก, ศ.พญ.กุลกัญญา โชคไพบูลย์กิจ. คำแนะนำการให้บริการวัคซีนในช่วงสถานการ์การระบาดของโรคติดเชื้อไวรัสโคโรนา 2019 (COVID-19). สมาคมโรคติดเชื้อแห่งประเทศไทยและสมาคมโรคติดเชื้อในเด็กแห่งประเทศไทย. 2020; 1-5
– Gamil et al., 2021. Pneumococcal disease in Thailand. International Journal of Infectious Diseases. Volume 102, January 2021, Pages 429-436
– Zhu X, et al. Co-infection with respiratory pathogens among COVID-2019 cases. Virus Res. 2020;285:198005
– S. Pooripussarakul, A. Riewpaiboon, D. Bishai, C. Muangchana, S. Tantivess
What criteria do decision makers in Thailand use to set priorities for vaccine introduction? BMC Public Health, 16 (2016), p. 684 -
The interventions I am considering putting in the model are the use of LLIN and IRS. Indonesian government has a national program to distribute LLINs as a malaria control. The coverage of LLIN and IRS in Rejang Lebong area will be based on the report from the National Malaria Control Program (NMCP).
From this meta-analysis study, the efficacy of LLIN is 54% compared to not using any LLINs at all (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877091/). LLINs are more effective in areas where the transmission occurred from the indoor biting mosquito species at night or early morning before people get up.
IRS has 62% efficacy as suggested by this study by Kim, et al (https://www.ajtmh.org/view/journals/tpmd/87/1/article-p117.xml), although the efficacy number may vary depending on the environment, initial malaria prevalence and the parasite that is dominant in the location. IRS also is most effective if the mosquito has the habit of indoor biting.
The interventions will be added as a new parameter so it will reduce the rate of transmission.
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Since my objective for the HBV modeling from week 2 is to study HBV transmission dynamic only and I didn’t include any intervention in my model. However, I would like to slightly modify HBV focus and restructure the modeling, by focusing on “HBV infection received from mother-to-child route” in Thailand. The intervention is antiviral prophylaxis. Therefore, the modified modeling is SEIR model which S= susceptible, IA = acute HBV infected from mother-to-child, Ic = Progress to Chronic HBV infection, and R = Recovered – considered as children having sufficient immune, E = exposure to antiviral (mothers and infants receiving antiviral). The similar research is https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13594-y
WHO recommends the use of antiviral prophylaxis for the prevention of hepatitis B transmission from mother-to-child. (https://www.who.int/news-room/fact-sheets/detail/hepatitis-b). Based on the meta-analysis, the efficacy of peripartum antiviral prophylaxis to reduce the risk of HBV mother-to-child transmission (tenofovir disoproxil fumarate, lamivudine, telbivudine) were ORs around 0·10 to 0·17 which could calculate as lower risk of infection for 73-90%. (https://hal.archives-ouvertes.fr/pasteur-03697722)
Please see my full report (the modified model and variables) here: https://tinyurl.com/mrxpyey9
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