COVID-19 Disease Modelling and Its Impact on Public Health Policy
Muhammad Qasim, University of Otago, New Zealand, Waqas Ahmad,University of Otago, New Zealand, Muhammad Azhar, Islamic International University Islamabad, Pakistan, Mohammad Azam Ali, University of Otago, New Zealand
In pandemics or outbreaks, mathematical models are one of the first tools to use for estimation, characterization, and planning of measures to mitigate the spread of disease.1 When a pandemic of influenza occurred in 2009, it was important to quickly analyze the potential of the virus to cause illness and death by comparing with already available data of 1918, 1957 and 1968 pandemics and characterizing it as mild or severe diseases.2 Exact forecasting of an epidemic or pandemic based on previous disease data is a major gold standard method of disease modelling. Additionally, modelling of real time data during an outbreak or pandemic is very helpful to pattern disease spread and identify the most vulnerable regions to take appropriate actions.3 Similarly, it is important to estimate the number of infected cases in the near future, for quick resource mobilization and resource management. These findings are very important for countries that are low in health security index to avoid sudden problems in their health system. Therefore, the use of mathematical modelling in certain areas of public health policy is becoming a gold standard for strategic decision linking with mitigation of pandemics.2 To use these mathematical models effectively, there is a need of good infrastructure for collection of real time data and close collaboration between data scientists and policy makers. Development of mathematical models for a general group of infectious diseases or a unique model for a specific disease helps to modulate information from different sources and perspective into a practical framework to solve complex problems associated with pandemics.4,5
These frameworks guide policy makers to take various intervention and public health measures and provide quantitative predictions of how interventions might affect the population’s health in the future. As in the 2009 influenza pandemic, the World Health Organization (WHO) used mathematical models for interpretation of early phase data of outbreak and made decisions accordingly for national outbreak response.2 These models also helped in later phases of the pandemic for strategic decisions about vaccinations, based on the estimation of doubling the number of infected cases and targeting the most vulnerable areas with respect to epidemic peak and timing.6 Similarly, during the large Ebola outbreak in West Africa, these models were used for estimation of key factors for controlling outbreaks such as number of expected cases and deaths, case doubling time, mortality and morbidity ratio, case isolation, contact tracing, quarantine and sanitary funeral management7. Upon the arrival of the Ebola vaccine, mathematical models helped researchers to design ring vaccination trails for successful vaccine testing with a minimum risk of exposure in late stages of the epidemic8.
Since the first confirmed cases of Corona-virus-2-2019 (COVID-19) reported in China, 2.1 million people have been infected and 146,088 deaths have occurred worldwide, according to WHO.9 Data scientists and epidemiologist across the globe are trying different mathematical models by considering various factors that impact the spread of COVID-19.10,11 . A prediction algorithm was developed which performs successive approximation based on real time data of COVID-19.12 In this model, we have defined the two sets of data to find the mean ratio (η) of present cases count to the sum of previous and new total cases on the current day. Based on this ratio we predicted the future burden of COVID-19 pandemic worldwide. One can see that the ratio will always be less than or equal to 1.0. The main reason lies in the fact that if the patient count at the current day becomes zero, the ratio (η) will converge towards 1.0. It will form a mathematical series of realistic data ranging from 0 to 1, calculated periodically with a period of one day. Then the mean of the series was calculated and used for future predictions. We have related the sequential change in previous days and used the average trend to predict the next number in the series.13 The conventional COVID-19 has three stages: monotone increase during the initial growth, exponential during the stability state and finally decay during the decline stage as shown in Figure 1 in the case of South Korea COVID-19 trends. For example, the COVID-19 trend of Korea shows that currently the country has expected to be in stage-3 with decaying trend, and the social protocol can be followed accordingly with a minimum loss in business and impact of social activities. In order to maintain the granularity of the predicted data, the death count was subtracted from the total patient counts and the number of recovered patients were further subtracted from the predicted value.
Our model predicts the near future behaviors of COVID-19 spreads worldwide and in countries as well.13 According to our COVID-19 model, the predicted maximum value of infected cases for Pakistan in the next 15 days (until May 15, 2020) is 43,150.00, and for deaths count, it is 1369.00 based on real time data provided by ministry of national health services regulations and coordination in Pakistan.14 The prediction accuracy is quite remarkable with minor error. It is very helpful to present the advance picture of possible pandemic scenarios to these handle situations in a better way. This is the reason public health institutes in the developed world formulated and utilized these mathematical models for public health policy advice and achieved remarkable results. For example, in the United Kingdom, the mathematical modelling unit of Public Health England works closely with the Ministry of Health while Robert Koch-Institute in Germany has its own unit of data scientists. Similarly, the Pasteur Institute in France, Center for Disease Control and Prevention (CDC) in USA, South Korea and China separately have disease-modelling experts to formulate policies for their countries. Therefore, we insist on public policy institutes to utilize these models as a frontline tool to estimate the COVID-19 crisis and make their policies effective.
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- G. M. Knight et al., “Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making,” International Journal of Infectious Diseases, vol. 42. Elsevier, pp. 17–23, 01-Jan-2016.
- N. E. Basta, D. L. Chao, M. E. Halloran, L. Matrajt, and I. M. Longini, “Strategies for pandemic and seasonal influenza vaccination of schoolchildren in the United States,” Am. J. Epidemiol., vol. 170, no. 6, pp. 679–686, 2009.
- J. P. Chretien, S. Riley, and D. B. George, “Mathematical modeling of the West Africa ebola epidemic,” Elife, vol. 4, no. DECEMBER2015, Dec. 2015.
- A. J. Kucharski, R. M. Eggo, C. H. Watson, A. Camacho, S. Funk, and W. J. Edmunds, “Effectiveness of ring vaccination as control strategy for Ebola virus disease,” Emerg. Infect. Dis., vol. 22, no. 1, pp. 105–108, Jan. 2016.
- “Situation reports.” [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/. [Accessed: 19-Mar-2020].
- Q. Lin et al., “A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action,” Int. J. Infect. Dis., vol. 93, pp. 211–216, 2020.
- J. T. Wu et al., “Application of the ARIMA model on the COVID-2019 epidemic dataset,” Data Br., vol. 93, no. January, pp. 211–216, 2020.
- M. Qasim, W. Ahmad, M. Yoshida, M. Gould, and M. Yasir, “Analysis of the Worldwide Corona Virus (COVID-19) Pandemic Trend;A Modelling Study to Predict Its Spread,” medRxiv, p. 2020.03.30.20048215, Apr. 2020.
- M. Qasim, W. Ahmad, S. Zhang, M. Yasir, and M. Azhar, “Data model to predict prevalence of COVID-19 in Pakistan,” medRxiv, p. 2020.04.06.20055244, Apr. 2020.
- “COVID-19 Health Advisory Platform by Ministry of National Health Services Regulations and Coordination.” [Online]. Available: http://covid.gov.pk/. [Accessed: 30-Apr-2020].
Muhammad Qasim is a PhD in Nano Bioengineering from Chung Ang University, Seoul South Korea and currently working as Senior Fellow at Centre of Bioengineering and Nanomedicine, University of Otago, Dunedin New Zealand. His areas of research are Infectious Diseases, Bioengineering, Nanotechnology and Regenerative medicine.
Waqas Ahmad is a PhD Scholar at department of Computer Sciences, University of Otago, Dunedin New Zealand. His areas of research interest are big data analysis and development of 5G MIMO beamforming system, Wireless IOT technologies, Medical Data and Disease modelling.
Muhammad Azhar is a PhD in Education from International Islamic University Islamabad and working as visiting faculty at same institute. He is Deputy Education Officer (DEO), Ministry of School Education, Punjab, Pakistan. His area of research is public health awareness and community education.
Azam specialises in biomaterials science and engineering. His research activities includes extraction and characterisation of biomaterials (e.g. proteins, peptides, polysaccharides, biologic tissues/collagen, etc.), understanding materials structure-functions and properties, designing/developing novel, advanced and nanostructure biomaterials & medical devices, functional packaging, formulation/coating and microencapsulation technology for health & wellbeing (both human and animal).
Internationally recognised as a biomaterials expert Azam has published more than 40 peer-reviewed papers, and many industrial (or clients) reports. At present he holds 18 international patents and patent specifications.
Steve Jones joined the Center for Information and Communication Sciences faculty in August of 1998. He came to Ball State University (BSU) from completing his doctoral studies at Bowling Green State University where he served the Dean of Continuing Education developing a distance-learning program for the College of Technology’s undergraduate Technology Education program. Dr. Jones was instrumental in bringing the new program on board because of his technical background and extensive research in the distance-learning field.
Prior to coming to higher education, Dr. Jones spent over sixteen and a half years in the communication technology industry. He owned his own teleconnect, providing high-end commercial voice and data networks to a broad range of end users. Dr. Jones provided all the engineering and technical support for his organization that grew to over twenty employees and two and a half million dollars per year revenue. Selling his portion of the organization in December of 1994, Dr. Jones worked briefly for Panasonic Communications and Systems Company as a district sales manager providing application engineering and product support to distributors in a five-state area prior to starting doctoral studies.