Modelling the Measles Outbreak at Hong Kong International Airport in 2019: A Data-Driven Analysis on the Effects of Timely Reporting and Public Awareness

Mathematical modeling of COVID-19 epidemic with effect of awareness programs

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel virus that emerged in China in late 2019 and caused a pandemic of coronavirus disease 2019 (COVID-19). The epidemic has largely been controlled in China since March 2020, but continues to inflict severe public health and socioeconomic burden in other parts of the world. One of the major reasons for China's success for the fight against the epidemic is the effectiveness of its health care system and enlightenment (awareness) programs which play a vital role in the control of the COVID-19 pandemic. Nigeria is currently witnessing a rapid increase of the epidemic likely due to its unsatisfactory health care system and inadequate awareness programs. In this paper, we propose a mathematical model to study the transmission dynamics of COVID-19 in Nigeria. Our model incorporates awareness programs and different hospitalization strategies for mild and severe cases, to assess the effect of public awareness on the dynamics of COVID-19 infection. We fit the model to the cumulative number of confirmed COVID-19 cases in Nigeria from 29 March to 12 June 2020. We find that the epidemic could increase if awareness programs are not properly adopted. We presumed that the effect of awareness programs could be estimated. Further, our results suggest that the awareness programs and timely hospitalization of active cases are essential tools for effective control and mitigation of COVID-19 pandemic in Nigeria and beyond. Finally, we perform sensitive analysis to point out the key parameters that should be considered to effectively control the epidemic.

Transmissibility of coronavirus disease 2019 in Chinese cities with different dynamics of imported cases

BACKGROUND

Monitoring the reproduction number (Rt ) of the disease could help determine whether there is sustained transmission in a population, but areas with similar epidemic trends could have different transmission dynamics given the risk from imported cases varied across regions. In this study, we examined the Rt of coronavirus disease 2019 (COVID-19) by taking different dynamics of imported cases into account and compared the transmissibility of COVID-19 at different intervention periods in Hangzhou and Shenzhen.

METHODS

We obtained the daily aggregated counts of laboratory-confirmed imported and local cases of COVID-19 infections in Hangzhou and Shenzhen from January 1 to March 13, 2020. Daily Rt and piecewise Rt before and after Wuhan lockdown were estimated, accounting for imported cases.

RESULTS

Since the epidemic of COVID-19 in Shenzhen was dominated by imported cases, Rt was around 0.1 to 0.7 before the Wuhan lockdown. After the lockdown of Wuhan and the initialization of measures in response to the outbreak, local transmission was well-controlled as indicated by a low estimated value of piecewise Rt , 0.15 (95% CI [0.09-0.21]). On the contrary, Rt obtained for Hangzhou ranged from 1.2 to 4.9 with a piecewise Rt of 2.55 (95% CI [2.13-2.97]) before the lockdown of Wuhan due to the surge in local cases. Because of the Wuhan lockdown and other outbreak response measures, Rt dropped below unity in mid-February.

CONCLUSIONS

Even though Shenzhen had more cases than Hangzhou, local transmission did not sustain probably due to limited transmission from imported cases owing to the reduction in local susceptibles as residents left the city during Chunyun. The lockdown measures and local outbreak responses helped reduce the local transmissibility.