Situation Report for COVID-19: Guinea-Bissau, 2022-07-03

Download the report for Guinea-Bissau, 2022-07-03 here.

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.


Epidemiological Situation

Total Reported Cases Total Reported Deaths Total Estimated Excess Mortality Estimated \(R_{eff}\)
8,369 171 2,795 0.57 (95% CI: 0.43-0.77)

Dominant Variants of Concern

**Figure 0: Timings of the modelled variants.**

Figure 0: Timings of the modelled variants.

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.


**Figure 1: Cumulative Deaths since 10 deaths.** Country not shown if fewer than 10 deaths.

Figure 1: Cumulative Deaths since 10 deaths. Country not shown if fewer than 10 deaths.


COVID-19 Transmission Modelling

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 45,971 (95% CI: 37,875-54,066) 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).


**Figure 2: Daily number of infections estimated by fitting to the current total of deaths.** Reported cases are shown in red. Model estimated infections are shown in blue (dark blue 50% interquartile range, light blue 95% quantile). The dashed line shows the current day.

Figure 2: Daily number of infections estimated by fitting to the current total of deaths. Reported cases are shown in red. Model estimated infections are shown in blue (dark blue 50% interquartile range, light blue 95% quantile). The dashed line shows the current day.

By fitting to the time series of deaths, we are able to estimate a time-varying reproduction number, \(R_{eff}\). \(R_{eff}\) is the the average number of secondary infections caused by a single infected person at a given time. If \(R_{eff}\) is above 1, the rate of transmission is increasing and the number of new infections is increasing. \(R_{eff}\) 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).



**Figure 3: Time-varying effective reproduction number, $R_{eff}$.** $R_{eff}$ (**green**) is the average number of secondary infections caused by a single infected person at time equal to $t$. A horizonatal dashed line is shown at $R_{eff}$ = 1. $R_{eff}$ < 1 indicates a slowing epidemic in which new infections are not increasing. $R_{eff}$ > 1 indicates a growing epidemic in which new infections are increasing over time. Dark green shows the 50% CI and light green shows the 95% CI

Figure 3: Time-varying effective reproduction number, \(R_{eff}\). \(R_{eff}\) (green) is the average number of secondary infections caused by a single infected person at time equal to \(t\). A horizonatal dashed line is shown at \(R_{eff}\) = 1. \(R_{eff}\) < 1 indicates a slowing epidemic in which new infections are not increasing. \(R_{eff}\) > 1 indicates a growing epidemic in which new infections are increasing over time. Dark green shows the 50% CI and light green shows the 95% CI


Using the model fit, we can forecast the expected trajectory for cumulative deaths assuming the transmission level, represented by the final \(R_{t}\) value stays the same over the next 28 days. N.B. Guinea-Bissau 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.


**Figure 4: Estimated daily deaths.** Projected deaths assuming the current level of interventions are maintained are shown in red (median and 95% quantile). Excess mortality is plotted in black. Includes a forecast of the next 28 days. The forecasted deaths in blue assumes healthcare capacity has been surged to ensure sufficient supply of ICU and hospital beds. The red curve assumes no surging in healthcare capacity and subsequently projects increased deaths.

Figure 4: Estimated daily deaths. Projected deaths assuming the current level of interventions are maintained are shown in red (median and 95% quantile). Excess mortality is plotted in black. Includes a forecast of the next 28 days. The forecasted deaths in blue assumes healthcare capacity has been surged to ensure sufficient supply of ICU and hospital beds. The red curve assumes no surging in healthcare capacity and subsequently projects increased deaths.


Short-term Epidemic Scenarios

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:

  • Scenario 1, Central: \(R_{t}\) will continue its median trend, reaching 0.988 (0.577, 2.55) by the end of the 28 day projection period.
  • Scenario 2, Optimistic: The \(R_{t}\) value will continue its \(25\%\) quantile trend, reaching 0.84 (0.471, 2.28). Representing either the implementation of new interventions, scaling up of interventions, or a reduction in mixing.
  • Scenario 3, Pessimistic: The \(R_{t}\) value will continue its \(75\%\) quantile trend, reaching 1.09 (0.688, 2.8). Representing either the relaxation of interventions, or an increase in mixing.

These trends were calculated by looking at all 28 day changes in the fitted \(R_t\) 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 86 (95% CI: 67-105) patients requiring treatment with high-pressure oxygen at the current date to 115 (95% CI: 47-182) 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 24 (95% CI: 21-27) patients requiring treatment with mechanical ventilation at the current date to 21 (95% CI: 16-26) 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.


**Figure 5: Healthcare demands in the next 28 days.** Individuals needing an ICU bed are assumed to need mechanical ventilation. Projected demand for Scenario 1 (Central) are shown in green . Projections for Scenario 2 (Optimistic) are shown in blue. Projections for Scenario 3 (Pessimistic) are shown in red. Current date shown with dashed line.

Figure 5: Healthcare demands in the next 28 days. Individuals needing an ICU bed are assumed to need mechanical ventilation. Projected demand for Scenario 1 (Central) are shown in green . Projections for Scenario 2 (Optimistic) are shown in blue. Projections for Scenario 3 (Pessimistic) are shown in red. Current date shown with dashed line.


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 1,576 (95% CI: 658-2,494) at the current date to 1,380 (95% CI: 480-2,279) by 2022-07-31. Under the pessimistic scenario, we estimate the daily number of infections will change from 1,576 (95% CI: 658-2,494) at the current date to 1,977 (95% CI: 890-3,063) by 2022-07-31.


**Figure 6: Daily number of infections estimated by fitting to deaths.** Projected infections for Scenario 1 (Central) are shown in green. Projections for Scenario 2 (Optimistic) are shown in blue. Projections for Scenario 3 (Pessimistic) are shown in red. Current date shown with dashed line.

Figure 6: Daily number of infections estimated by fitting to deaths. Projected infections for Scenario 1 (Central) are shown in green. Projections for Scenario 2 (Optimistic) are shown in blue. Projections for Scenario 3 (Pessimistic) are shown in red. Current date shown with dashed line.

Legacy Short Term Scenarios

Legacy scenarios with a flat \(50\%\) increase/decrease to \(R_t\) are presented here.

**Figure 7: Healthcare demands in the next 28 days.** Individuals needing an ICU bed are assumed to need mechanical ventilation. Projected demand for Maintain (the epidemic continues to grow at the current rate) are shown in green. Projections for Migitation (a further 50% reduction in transmission) are shown in blue. Projections for Reverse (relaxing interventions by 50%) are shown in red. Current date shown with dashed line.

Figure 7: Healthcare demands in the next 28 days. Individuals needing an ICU bed are assumed to need mechanical ventilation. Projected demand for Maintain (the epidemic continues to grow at the current rate) are shown in green. Projections for Migitation (a further 50% reduction in transmission) are shown in blue. Projections for Reverse (relaxing interventions by 50%) are shown in red. Current date shown with dashed line.

**Figure 8: Daily number of infections estimated by fitting to deaths.** Projected infections for Maintain (the epidemic continues to grow at the current rate) are shown in green. Projections for Mitigation (a further 50% reduction in transmission) are shown in blue. Projections for Reverse (relaxing interventions by 50%) are shown in red. Current date shown with dashed line.

Figure 8: Daily number of infections estimated by fitting to deaths. Projected infections for Maintain (the epidemic continues to grow at the current rate) are shown in green. Projections for Mitigation (a further 50% reduction in transmission) are shown in blue. Projections for Reverse (relaxing interventions by 50%) are shown in red. Current date shown with dashed line.