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## Goal/Experiment:
Assess and compare the probability of tuberculosis transmission in three Thai prisons based on five dynamic models.

## Assessment of Tuberculosis Transmission Probability in Three Thai Prisons Based on Five Dynamic Models

**DOI:** [dx.doi.org/10.17504/protocols.io.6qpvr868zlmk/v1](dx.doi.org/10.17504/protocols.io.6qpvr868zlmk/v1)

**Authors:**
- Nithinan Mahawan, Thanapoom Rattananupong, Puchong Sri-Uam, Wiroj Jiamjarasrangsi  
  1Faculty of Medicine, Chulalongkorn University  
  2Center for Safety, Health and Environment of Chulalongkorn University

**Corresponding Author:**
- Nithinan Mahawan, Faculty of Medicine, Chulalongkorn University

**Protocol Status:** Working

**Created:** May 05, 2024  
**Last Modified:** June 25, 2024  
**Protocol Integer ID:** 99258  

**Keywords:** Probability of tuberculosis transmission, dynamic models, model parameters, prisons

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## Abstract
This study aimed to assess and compare the probability of tuberculosis (TB) transmission based on five dynamic models: the Wells–Riley equation, two Rudnick & Milton-proposed models based on air changes per hour (ACH) and liters per second per person (L/s/p), the model proposed by Issarow et al., and the Applied Susceptible-Exposed-Infected-Recovered (SEIR) TB transmission model. The study aimed to determine the impact of model parameters on such probabilities in three Thai prisons. The results revealed that the median (Quartiles 1 and 3) of TB transmission probability among these cells was 0.052 (0.017, 0.180). Compared to the pioneering Wells–Riley model, the remaining models projected a discrepant TB transmission probability from less to more commensurate to the degree of model modification. The ventilation rate and the number of infectious TB patients were the greatest impact factors on the estimated TB transmission probability. All stakeholders must urgently address these influential parameters to reduce TB transmission in prisons.

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## Materials and Methods

### Materials
1. **Absolute Ventilation Rate Measurement:**
   - Using the Kimo HQ210 with SCOH 112 probe (Sauermann Industries, ZA Bernard Mouliend, Montpon, France) for measuring CO₂ concentrations.
   
2. **ACH (Air Changes per Hour):**
   - Classical metric assessing infection control risk; wind speed in cells using a hot wire thermo-anemometer (Model SDL350, Extech Instruments, Waltham, MA).

### Before Start
1. **Literature Review Goals:**
    - Incidence and prevalence of TB in prisons globally and in Thailand.
    - Evidence of prisons as TB reservoirs.
    - Humanitarian issues and health problems.
    - Factors affecting TB transmission in prisons.
    - Comparison of dynamic models of TB transmission.
    
2. **Contact Department of Corrections:**
   - Walkthrough to assess suitability in June 2019.

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## Research Ethics
1. Ethical approval from Chulalongkorn University Faculty of Medicine (Ref: 610/63).
2. Permission from the Department of Corrections, Ministry of Justice needed.
3. Inform and gain consent from relevant stakeholders, but inmate personal information not required for the study.

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## Data Collection
1. Train research team on protocols.
2. CO₂ collection inside and outside cells using Kimo HQ210; measured by trained inmates and researchers.
3. Measure wind speeds with thermo-anemometer (Model SDL350).
4. Collection of infection data by health volunteers.
5. Survey of cell architecture and characteristics.
6. Literature review for model parameters.

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## Statistical Analysis
1. **Estimation of TB Transmission Probability:**
   - Calculation based on dynamic models.
   - Assess agreement using Spearman’s rank correlation.
   - Bland–Altman analysis for agreement pattern.
   - Model parameter influence using Wilcoxon rank-sum (Mann-Whitney test) and linear regression.
   
2. **Parameters Analysed:** 
   - Ventilation rates.
   - Number of infectious inmates.

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## Results and Conclusion
1. **Variant Model Projections:**
   - Different transmission probabilities across models.
   - Wells–Riley model as reference showed low to high probability variance.
   
2. **Risk Factors:**
   - Key factors: low ventilation rates, high TB inmate numbers.
   - Models: Similar patterns of TB transmission probability with varying high and low extremes.
   - Urged addressing ventilation and inmate number issues.
   
3. **Recommendations:**
   - Validation and further studies required for more accurate TB incidence prediction in prison settings.

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## Protocol References
1. World Health Organization. Global tuberculosis report 2023. Geneva: WHO; 2023.
2. United States Agency International Development. Tuberculosis in prisons: a growing public health challenge [Internet]. USAID; 2014 [Cited 2021 August 8].
3. World Health Organization [Internet]. Tuberculosis: Key facts. [Cited 2021 August 8].
4. World Prison Brief, Institute for Crime & Justice Policy Research, Birkbeck University of London. 
5. Walter KS, Martinez L, et al. Lancet. 2021;397(10284):1591-6.
6. Mabud TS, de Lourdes Delgado Alves M, et al. PLoS Med. 2019;16(1):e1002737.
... [Additional 30 references] ...

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