Widespread community transmission of the Covid-19 outbreak would be the worst public health nightmare, with rising demands on the health systems with alarming morbidity and mortality (World Health Organization 2020a). Trying to visualize how such a scenario would look like must not be ignored, only reason being the denial of the possibility of such an occurrence. At the outset, it must be noted that predictions are estimates to be used by health systems planners wisely, in the backdrop of many other factors. Unique demographic factors, health seeking behaviours and health system characteristics of a country should be taken in to consideration when applying predictions into actual public health interventions. Further, the methods used to predict the progress of Covid-19 has been quite diverse and debated (Anastassopoulou et al. 2020; Cyranoski 2020; Roda et al. 2020). On the contrary, such drawbacks should not prevent the use of existing methods in visualizing the progress of the outbreak. The results of such predictions should be seen as predictive evidence to formulate public health decisions, than attempts to find faults of health systems and prevailing public health interventions, hence should create a constructive dialogue and a productive debate directed towards pre-emptive, stepwise augmentation of capacity (Weissman et al. 2020).
During the current study, we used an application using the SIR modes, which is one of the simplest compartmental epidemiological models available (Environmental Systems Research Institute (ESRI) 2020; Weissman et al. 2020). The susceptible populations were limited to specific groups since the beginning of Covid-19 in Sri Lanka, starting off with returnees from high risk countries to substance abusers to returning migrants to armed forces (Director General of Health Services 2020). Nevertheless, when the Covid-19 outbreak goes to the community spread, the community will behave more or less in a homogeneous way. Hence, the application of the SIR model in homogeneous populations could be justified during widespread community transmission.
During this study, we used a population-based approach in visualizing how the Covid-19 community spread and beyond would look like. Instead of using the past epidemiological data as the basis of the predictions, we simulated the spread of the disease by introducing one case of Covid-19 case at the same time to all RDHS areas. This would be very unlikely in the real-life scenario, however what we wanted to do in this exercise was to have a baseline idea of how widespread community spread would look, if it happens in Sri Lanka. In addition, this was probably the “best” of the worst-case scenarios, since each health districts gets only one case of Covid-19 to start off with, which is, obviously too optimistic to occur in real life. What this implies is that, if the “best” of the worst-case scenarios is alarming, the worse of the worst-case scenarios would be much alarming and demanding. Nevertheless, such a “best” of the worst case scenarios would be more influential in advocating for preparedness, hence all health systems are inherently reluctant to accept that their systems could fail in times of crisis.
According to the predictions, the peak of the outbreak will occur around Day 218 – 220 (after about seven months of the commencing of the community transmission. At the peak, the country may need around 1942, 583 and 388 beds, ICU beds and ventilators per day at the peak. As per the Ministry of Health reports, the total number of hospital beds for treatment purposes in the country as per 30.10.2020 is to be around 5485 (Director General of Health Services 2020). In the meantime, toal ICU beds in use to be at 146. It is evident seen that even if the community transmission occurs the country may have challenges in catering to the need of beds, ICU beds and ventilators that may arise.
Even though the current policy of Sri Lanka is to admit all patients who are diagnosed with Covid-19 to be admitted to a desingated Covid-19 hospital, this may not continue to be feasible in time of a community transission with increased demand. Hence, it was decided to use the 2.5% of default value of hospitalization provided in the model, based on statistics from the USA, which is currently going through a community transmission. If the current decision of admitting all Covid-19 patients to a hospital without a triage system, it is likely that the hospitals will be overwhelmed even prior to the peak of the community transmission.
It should be noted that during the current model, artificial demarcation between the stages prior to community transmission and beyond has been made. The number of cases has been reset to zero prior to running the model during the community transmission and beyond. Even though it would be difficult to distinguish between the onset of community transmission and the preceeding stages, for the purpose of the current model, this approach was adopted. In retrospect, the total number of cases during the community transmission and beyond is likely to be much higher than during the preceding stages, hence this approach could be justified.
CHIME model has been added value by incorporating it into the ArcGIS Pro software, hence it was possible to carry out the model for each of the RDHS areas. Even though during the current study, the model was run at RDHS level, the same could be done at Medical Officer of Health areas (The grass root level health administrative division) as per the requirement.
However, one possible significant draw back of the CHIME model, as stated out by its developers from the PENN State University was that it recommends only a maximum prediction period of 30 days, however, if the peak does not occur within that period, it is recommended to increase the duration accrodingly(Penn Medicine 2020).It is likely that as one moves further in time, more uncertain the projections become, however, projections still could be useful. We reemphasize the point that we mentioned at the beginning of the discussion on model estimates here. The results of this model should only be treated as estimations, as they are highly sensitive to the model assumptions, data and parameters that we use. Having given consideration for the above facts, during the current study, we used a prediction period of 365 days which was able not only to capture the peak, but alsso the return of the curve to the baseline.
Further research needs to be carried out incorporating factors other than the population such as the actual case number, socioeconomic and demographic vulnerabilities and health systems capacities in predicting widespread community spread in Sri Lanka.