Description
ABSTRACT
Background: Infectious and non-infectious diseases are major causes of economic and social
victims in many countries. According to the WHO, incidence of Tuberculosis (TB) is worldwide
causing mortality of approximately 1.3 million people each year. While in case of Crimean Congo
Hemorrhagic Fever (CCHF), mortality rate is increasing in different countries from 10-40%.
Although, the diagnosis and chemotherapy are well-known for infectious diseases but prediction
of any infectious disease through mathematical modelling can play a vital role in investigation,
forecasting and controlling epidemic.
Materia& Methods: Study was conducted to predict CCHF in Pakistan and TB in District
Muzaffargarh, District Chakwal and District Faisalabad by using Susceptible-Infected-Recovered
(SIR) model. To this end, pilot survey was conducted to check the validity of SIR model followed
by formal and informal testing. Then actual data was collected through different sources such as
hospital statistics, registers of diseases and primary health care consultation.
Results: Predicted cases obtained by putting the collected data into SIR model mathematical
equation was too close to actual number of cases reported during different years.
Conclusion: With the future prediction of these diseases, it will be less challenging to break the
chain of disease transmission, reducing the morbidity and mortality rate through early preventions
and decreasing the country’s economic losses.
Key Words: SIR model, Prediction, Tuberculosis
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