Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020
2.1. SEIR Model for Peak Prediction
dE(t)/d(t) = βS(t)I(t) − αE(t),
dI(t)/d(t) = αE(t) − γI(t) − MI(t),
dR(t)/d(t) = γI(t),
dD(t)/d(t) = MI(t)
2.2. β. Estimation Using GA
- Population initialization: In order to find a solution to the problem of the cost function, the GA initially creates a number of populations that randomly encodes the chromosomes (individuals). Then, the cost values of the generated population are evaluated.
- Selection: In this process, each individual identified by its associated cost is ranked and the corresponding individual fitness is selected. According to fitness, the best chromosomes from the population are then selected such that better fitness has a bigger chance to be selected. Subsequently, the solutions selected from one population are implemented to form a new population. This process is motivated by the new population potentially being better than the previous one. The selection process is performed using a certain function that fixes the generation gap. The selected individuals are then recombined.
- Crossover: To make new offspring (children) for the following iteration, the selected individuals (parents) have to undergo a crossover with a crossover probability. However, if there is no crossover performed, the offspring is an exact copy of the parents.
- Mutation: In this process, the information in the chromosomes is randomly modified. The genes occasionally mutate to be converted to novel genes. Based on mutation, it is possible to control the multifariousness of the population as well as to enhance the search capacity of the search scheme.
- Evaluation: For each individual, the cost function of the optimization problem is calculated. The stopping criterion of the GA is the number of iterations after which the process is stopped. For each iteration, the β value that has the minimum cost function is recorded. The distribution of the β values is then approximated by a normal distribution with a mean and standard deviation.
2.3. ANFIS for Short-Term Forecasting
3.1. Infection Rate (β) Estimation
3.2. Epedimic Peak Prediction
3.3. Epidemic Peak after Possible Interventions
3.4. Short-Term Forecasting
- The number of people who had contact with COVID-19 patients is enormous, as reported in . This could make the process of tracking and isolating more complex. Based on the information reported by Chinese medical doctors involved in Wuhan, the critical cases form 10% of the total number of infected people. The early diagnosis and treatment would reduce the flow of COVID-19 patients into the ICU unit .
- Poor experience in treating and managing cases with different levels of infection. For instance, severe cases should be kept under monitoring with intensive care, while mild cases without clear symptoms should be kept with less intensive care in the hospitals. However, patients under investigation should be placed in special isolation outside the hospitals. This kind of management would ease the treating process with the currently available equipment .
- The current MCO implemented in Malaysia is limited to aiding the awareness of the people to the danger of COVID-19. For the first 10 days of the MCO, 60% of the public has obeyed the MCO issued by the government . Thus, more restrictions are needed to enforce the MCO. By increasing the public awareness, the infection rate will be reduced, which would result in decreasing the reproductive number and delaying the epidemic peak.
Conflicts of Interest
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|N||Malaysia population||32.6 × 106|
|Population size||200||Mutation rate||0.02|
|Number of iterations||1000||Mutation percentage||0.9|
|Fuzzy structure||Sugeno-type||No. of epochs||300|
|Rules clustering||Grid partition||Input||Day number|
|MF type||Gaussian||Output||Infected cases|
|Optimization method||Hybrid||Output MF||constant|
|Parameter||Training Data||Testing Dataset|
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Alsayed, A.; Sadir, H.; Kamil, R.; Sari, H. Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020. Int. J. Environ. Res. Public Health 2020, 17, 4076. https://doi.org/10.3390/ijerph17114076
Alsayed A, Sadir H, Kamil R, Sari H. Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020. International Journal of Environmental Research and Public Health. 2020; 17(11):4076. https://doi.org/10.3390/ijerph17114076Chicago/Turabian Style
Alsayed, Abdallah, Hayder Sadir, Raja Kamil, and Hasan Sari. 2020. "Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020" International Journal of Environmental Research and Public Health 17, no. 11: 4076. https://doi.org/10.3390/ijerph17114076