MRC Centre for Global Infectious Disease Analysis, Imperial College London
This report uses excess mortality data for the period of the COVID-19 epidemic (December 2019 onwards). These numbers are calculated by comparing current mortality to historic trends. These data are then used to back-calculate an ‘inferred number of COVID-19 infections’ using mathematical modelling techniques (see Methods for further details) to estimate the number of people that have been infected and to make short-term projections for future healthcare needs. Not all countries are able to provide timely estimates of excess mortality, so estimates from the The Economist Excess Deaths Model are harnessed to fill in these gaps. Data on reported deaths and cases are from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. These are updated daily and whilst there may be a short delay, they are generally consistent with Ministry reports.
Total Reported Cases | Total Reported Deaths | Total Estimated Excess Mortality | Estimated Reff |
---|---|---|---|
15,690 | 189 | 5,707 | 1.03 (95% CI: 1-1.06) |
This report adjusts for the Delta, Omicron, Omicron Sub-Variant variants. The timings of which are shown in Figure 1. These dates are based upon reported sequencing data from NextStrain and GISAID. For countries with a limited number of recent sequences, these dates are inferred from global or regional trends and should be interpreted with caution.
The figure below shows the cumulative reported deaths as a function of the time since the 10th death was reported. Dashed lines show the expected trajectory for different doubling times of the epidemic. For example, with a doubling time of 3 days, if there are currently a total of 20 deaths reported, we would expect there to be 40 deaths in total reported in 3 days-time, 80 deaths in 6 days-time, 160 deaths in 9 days-time etc. For most epidemics, in the absence of interventions, we expect a doubling time of 3-4 days for this disease.
The figure below shows the estimated number of people infected over the past 4 weeks. The bar charts show, for comparison, the number of reported cases. We estimate that there has been a total of 94,324 (95% CI: 88,731-99,916) infections over the past 4 weeks. The right-hand plot shows these data on a different scale as the estimated infections are likely to be much larger than the reported cases. Importantly, the estimated infections includes both asymptomatic and mild cases that would not necessarily be identified through surveillance. Consequently, the estimated infections are likely to be significantly higher than the reported cases in all countries (see our FAQ for further explanation of these differences and why the reported cases and estimated infections are unlikely to match).
By fitting to the time series of deaths, we are able to estimate a time-varying reproduction number, Reff. Reff is the the average number of secondary infections caused by a single infected person at a given time. If Reff is above 1, the rate of transmission is increasing and the number of new infections is increasing. Reff is assumed to change proportionally to mobility. By fitting our model to excess mortality we aim to account for under-ascertainment of COVID-19 related deaths (please see our FAQ section for more information about this approach).
Using the model fit, we can forecast the expected trajectory for cumulative deaths assuming the transmission level, represented by the final Rt value stays the same over the next 28 days. N.B. Djibouti is forecast to be close to or surpassing our best estimates for healthcare capacity in the next 28 days. Estimates of deaths in the next 28 days may be inaccurate due to our working assumptions for mortality in individuals who do not receive appropriate treatment. See our methods for more information.
The following scenarios do not account for future VoC and assumes the current VoC (based on sequence data) remains dominant.
We include short-term projections of healthcare demand and new infections under the following three scenarios:
These trends were calculated by looking at all 28 day changes in the fitted Rt values and then calculating the relevant quantiles within each replicate.
We estimate that over the next 4 weeks demand for hospital beds will change from 283 (95% CI: 262-304) patients requiring treatment with high-pressure oxygen at the current date to 178 (95% CI: 167-189) hospital beds being required on 2022-07-31 if no further interventions are introduced (Scenario 1). Similarly, we estimate that over the next 4 weeks demand for critical care (ICU) beds will change from 58 (95% CI: 57-58) patients requiring treatment with mechanical ventilation at the current date to 53 (95% CI: 52-53) by 2022-07-31. These projections are dependant on the chosen age-dependant hospitalisation rates (see Methods). N.B. These scenarios are unlikely to show significant differences for the first week since there is a delay of approximately 10 days between infection and hospital admission. Consequently, the effectiveness of a change in policy is likely to be better captured by hospital admission data approximately 2 weeks after the policy change is implemented.
The impact of each scenario has a more immediate effect on the daily number of infections. The figure below shows the impact of each scenario on the estimated daily incidence of new infections. If interventions are scaled up (Scenario 2), the daily number of infections will change from 2,905 (95% CI: 2,750-3,060) at the current date to 878 (95% CI: 840-915) by 2022-07-31. Under the pessimistic scenario, we estimate the daily number of infections will change from 2,905 (95% CI: 2,750-3,060) at the current date to 2,445 (95% CI: 2,366-2,524) by 2022-07-31.
Legacy scenarios with a flat 50% increase/decrease to Rt are presented here.