Infection transmission and chronic disease models in the study of infection-associated cancers

DETERMINING PROBABILITY OF CANCER CELL TRANSFOMATION AT HUMAN PAPILLOMAVIRUS INFECTION

Aim. The purpose of the work was to assess the probability of cancerous transformation of cells for viruses of high and low oncogenic risk. Aim. The purpose of the work was to assess the probability of cancerous transformation of cells for viruses of high and low oncogenic risk. Results. Using normalized squared error (NSE) for viruses of high (20 strains) and low (153 strains) oncogenic risk, rank statistic of 2-exponential type was build. For productive papillomavirus infection, NSE function was determined as the growing accurate 2-exponent of a cell layer basal to the epithelial surface. Logarithm of NSE numerical values is proportional to the cell entropy that is connected with the availability of virus DNA. To calculate entropy, generalized Hartley formula was used with the informational cell of dimension d: H = NdLOG(NSE), where N is the generalized cell coordinate. Conclusions. Using a statistical ensemble of E6 proteins separately for viruses of high and low oncogenic risk made it possible to assess the probability of cancerous transformation of cells, which was proportional to the ratio of the area of entropy of cancer transformation to the area of the productive entropy region papillomavirus infection.

Towards a new approach for managing pandemics: Hybrid resilience and bowtie modelling

Pandemic viruses have historically caused tremendous damage to lives and livelihoods. The coronavirus, COVID-19, has proven to be a significant issue around the world. In this paper it is argued that systems of controlling similar types of disasters need to be improved through learning from past experience and from others, as well as through improved modelling for better decision making. In doing so, the focus will be on resilience modelling and learning from incidents. Therefore, in this paper, first the introduction deals with hybrid approaches in operational research highlighting the differences between hybrid modelling and hybrid models. Second, an introduction to mathematical modelling of epidemics is provided and how such modelling leads to certain types of public health modelling is demonstrated. Third, resilience modelling will be discussed as a complimentary type of modelling, where concepts related to robustness, redundancy, resourcefulness, and rapidity are introduced. Fourth, resilience modelling will be extended to new principles taking COVID-19 as an example for the analysis. Fifth, the analysis will then be used to compare degrees of resilience for different countries. Finally, other modelling approaches for managing - and learning from - pandemics, in terms of root cause analysis, bowtie modelling and safety barriers, are proposed.

A Dynamic Mathematical Modeling Revelation about the Impact of Vaccination on Hepatitis B Virus-induced Infection and Death Rate in Bangladesh

Aim

Attainment of sustainable development goal (SDG) targets requires reducing the rate of new hepatitis B virus (HBV)-induced infection and mortality rate to 90% and 65%, respectively, by 2030. Therefore, it is important to investigate the feasibility of reducing the required rates of HBV-induced infection and death incidents at the current rate of vaccination coverage in Bangladesh. Moreover, factors influencing vaccination coverage like negative bias toward girls during immunization can affect the current vaccination program and ultimately hinder the efforts to reduce HBV-induced infection and death rates. To investigate the possibility of reducing HBV-induced infection and death rates with current vaccination coverage, we adopted mathematical molding-based approach.

Materials and methods

We developed a mathematical model based on the susceptible–infectious–recovered model to simulate the HBV-induced infection in children under the age of five at three different vaccination rates: 80, 90, and 95%. Additionally the impact of current vaccination coverage was assessed on HBV-induced death rates in the future. Moreover, we took advantage of the mathematical model to investigate the impact of negative bias toward girls in vaccination program on HBV-induced infection and death rates.

Results

The model simulations revealed that 10% increase in the vaccination rate from 80 to 90% can potentially contribute to the significant lowering (around 40%) of HBV-induced infection rate among children. When increased by 5% of vaccination rate from 90 to 95%, the HBV-infection rate is likely to be decreased by another 22%. Likewise, 44% reduction in HBV-induced death rate in the future (2050 onward) can potentially be achieved by 10% increase in the current vaccination rate from 80 to 90%, whereas 5% increase in the current vaccination rate (90–95%) may lead to 24% further reduction of death rate. These results underscored the significant impact of vaccination in reducing HBV-induced infection among children and future death rates in adults. Moreover, at 90% vaccination coverage, the negative bias of vaccination toward girls contributes to an increase of 15 and 12% of HBV-induced infection and death rates, respectively, in female subjects compared to their male counterparts.

Conclusion

The current vaccination coverage (80–90%) is further aggravated by untimely vaccination, dropouts from vaccination program, and negative bias toward girls in vaccination program. Therefore, if the current situation persists, it will not be possible to accomplish the required reduction in HBV-induced infection and death rates by 2030, according to the SDG guidelines. Moreover negative bias in the vaccination program may intensify the HBV-induced infection and death rates in the future.

Clinical significance

In light of the mathematical model, we suggest that the vaccination coverage should be increased to 95% without any negative bias toward girls. To accomplish this, the concerning authorities must ensure timely and full completion of the HBV vaccine schedules, reducing dropouts from vaccination program, and lastly preventing negative bias toward girls to uplift vaccination coverage to more than 95% with gender equality. Without these strategies, the necessary reduction in the HBV-induced infection and death rates in Bangladesh may not be attained per SDG directives.

How to cite this article

Chakraborty S, Chakravorty R, Alam S, et al. A Dynamic Mathematical Modeling Revelation about the Impact of Vaccination on Hepatitis B Virus-induced Infection and Death Rate in Bangladesh. Euroasian J Hepato-Gastroenterol 2019;9(2):84–90.

Different Challenges in Eliminating HPV16 Compared to Other Types: A Modeling Study

Background

Human papillomavirus (HPV) vaccination is still not reaching many high-risk populations. HPV16/18 vaccines offer cross-protection against other types, for example, HPV45. Both direct vaccine efficacy and indirect herd protection contribute to vaccination effectiveness.

Methods

We used a dynamic transmission model, calibrated to cervical screening data from Italy, to estimate vaccination effectiveness against HPV16 and HPV45 infection, assuming for HPV45 either 95% or lower cross-protection.

Results

Basic reproductive number was smaller (2.1 vs 4.0) and hence vaccine effectiveness and herd protection stronger for HPV45 than for HPV16. The largest difference in the reduction of infection prevalence in women <35 years old was found at 70% coverage in girls-only vaccination programs (99% vs 83% for total protection for HPV45 and HPV16, respectively, mainly owing to stronger herd protection, ie, 37% vs 16%). In gender-neutral vaccination, the largest difference was at 40% coverage (herd protection, 54% vs 28% for HPV16 and HPV45, respectively). With ≥80% coverage, even 50% cross-protection would reduce HPV45 by ≥94%.

Conclusions

The characteristics of individual high-risk HPV types strongly influence herd protection and determine the level of coverage and cross-protection required to reduce or eliminate the infection through HPV vaccination. HPV16 infection and related cancers are the most difficult to eliminate.