Abstract:
As new viruses and new pandemics emerge we face the question as to whether
our global health systems are well prepared to deal with them. Non pharma ceutical measures are a key control measure in the battle against infectious
diseases especially in the absence of vaccines or when available vaccine
quantities are not sufficient. The 2014-2016 West African outbreak of Ebola
Virus Disease (EVD) was the largest and most deadly to date. Contact tracing,
following up those who may have been infected through contact with an infected
individual to prevent secondary spread, plays a vital role in controlling such
outbreaks. Our aim in this work was to mechanistically represent the contact
tracing process to illustrate potential areas of improvement in managing
contact tracing efforts. We also explored the role contact tracing played in
eventually ending the outbreak. We presented a system of ordinary differential
equations to model contact tracing in Sierra Leonne during the outbreak.
We included the novel features of counting the total number of people being
traced and tying this directly to the number of tracers doing this work. Our
work highlighted the importance of incorporating changing behavior into one’s
model as needed when indicated by the data and reported trends. Our results
showed that a larger contact tracing program would have reduced the death
toll of the outbreak. Counting the total number of people being traced and
including changes in behavior in our model led to better understanding of
disease management.
Viral outbreaks differ in many ways, despite these differences policy responses
used to tackle viral epidemics tend to be similar across time and countries.
Substantial progress has been made since the 2014-2016 Ebola outbreak with
lessons learnt from previous and ongoing outbreaks followed by significant
investments into surveillance and preparedness and this has been of help in
dealing with the COVID-19 pandemic. We formulated a mathematical model
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for the spread of the coronavirus which incorporated adherence to disease
prevention. The major results of this study were: first, we determined optimal
infection coefficients such that high levels of coronavirus transmission were
prevented. Secondly, we found that there existed several optimal pairs of
removal rates, from the general population of asymptomatic and symptomatic
infectives respectively that could protect hospital bed capacity and flatten
the hospital admission curve. Of the many optimal strategies, this study
recommended the pair that yielded the least number of coronavirus related
deaths. The results for South Africa, which is better placed than the other
sub-Sahara African countries, showed that failure to address hygiene and
adherence issues will preclude the existence of an optimal strategy and could
result in a more severe epidemic than the Italian COVID-19 epidemic. Relaxing
lockdown measures to allow individuals to attend to vital needs such as food
replenishment increases household and community infection rates and the
severity of the overall infection.
Although the tobacco epidemic is one of the biggest health threats, responsible
for more than 8 million deaths annually with 15% of these caused by second
hand smoke , only a few mathematical models have addressed smoking in the
context of lung cancer. In our work we present two models, a stochastic model
and a deterministic model both of which are fitted to actual smoking data. The
expected solution of the stochastic model predicts a steady state solution in the
long run for the moderate and heavy smokers with proportions of these popu lations remaining to sustain the habit contrary to the trend in the actual data
which suggests extinction of these populations. The deterministic model, re vealed that the presence of highly quantifiable efficacious control measures can
reduce the lung cancer load by 50% although the number of lung cancer deaths
would remain the same for sometime. These results confirm the conclusions
of the stochastic model and reveal further that these control measures can re duce the lung cancer load and lung cancer deaths by about 50% if there is a
reduction of at least 20% in the population of susceptible individuals taking up
smoking. Specifically, if the number of new potential (susceptible) smokers ex xiv
ceeds a quantifiable threshold, Λ then even if R0 < 1 there is persistence of
the epidemic.
Hellen, M (2024). Mathematical modeling of infectious and non communicable diseases: exploring public health intervention strategies. Afribary. Retrieved from https://tracking.afribary.com/works/mathematical-modeling-of-infectious-and-non-communicable-diseases-exploring-public-health-intervention-strategies
Hellen, Machingauta "Mathematical modeling of infectious and non communicable diseases: exploring public health intervention strategies" Afribary. Afribary, 30 Mar. 2024, https://tracking.afribary.com/works/mathematical-modeling-of-infectious-and-non-communicable-diseases-exploring-public-health-intervention-strategies. Accessed 10 Nov. 2024.
Hellen, Machingauta . "Mathematical modeling of infectious and non communicable diseases: exploring public health intervention strategies". Afribary, Afribary, 30 Mar. 2024. Web. 10 Nov. 2024. < https://tracking.afribary.com/works/mathematical-modeling-of-infectious-and-non-communicable-diseases-exploring-public-health-intervention-strategies >.
Hellen, Machingauta . "Mathematical modeling of infectious and non communicable diseases: exploring public health intervention strategies" Afribary (2024). Accessed November 10, 2024. https://tracking.afribary.com/works/mathematical-modeling-of-infectious-and-non-communicable-diseases-exploring-public-health-intervention-strategies