<strong>качественное применение модели информационной мотивации поведенческих навыков</strong>

Низкое влияние COVID-19 в Африке

Реальное влияние пандемии COVID-19 в Африке

Age-Patterns of COVID-19 Severity in Lusaka Compared to Other Countries

Оценки избыточной смертности ВОЗ за 2020 год в Замбии

Средняя оценка ВОЗ избыточной смертности в 290 смертей на миллион человек в Замбии за 2020 год занимает 26-ое место среди 47 стран региона Африки ВОЗ (рис. 2а). Эта оценка схожа с эквивалентной оценкой региона Африки в 320 смертей на миллион, которая занимает 5-ое место, выше только региона Западной Тихоокеанской, где оценка избыточной смертности за 2020 год была отрицательной. Диапазон неопределенности для Замбии широк (и схож по величине с 40 другими африканскими странами, в которых ВОЗ не смогла идентифицировать достаточные данные о смертности), что позволяет лишь ограниченное сравнительное анализ с остальным миром. Верхняя граница для Замбии в 820 смертей на миллион исключает оценки нижней границы для Алжира и Южной Африки (обе из которых имели документированные масштабные эпидемии40) и регионов Европы и Америк ВОЗ. С другой стороны, нижняя граница в -230 смертей на миллион превышает верхние оценки для Сейшельских о-вов, Маврикия, Кении, Того и охватывает оценки для региона Западной Тихоокеанской. Сейшелы и Маврикий, две более развитые о-вные нации в регионе Африки, известны тем, что они испытали более низкие, чем обычно, уровни общей смертности за 2020 год (вероятно, в связи с мерами подавления, включающими строгие контрольные меры на границе40,41). Помимо этих сравнений, мало что можно сказать о фактической избыточной смертности в любой стране с такими же широкими интервалами достоверности.

Fig. 2: Global estimates of excess mortality relative to patterns of demographic vulnerability.

Figure shows a World Health Organisation (WHO) estimates of excess mortality per million people in 2020. Points show mean and lines 95% confidence intervals from 1000 samples. Crosses show confirmed COVID-19 mortality per million people in 2020. b Estimates of region-level IFR calculated using age-specific IFR estimates from Brazeau et al.42 weighted by region population age-distribution (i.e., assuming infection equally distributed across the population). Points show median IFR and lines 95% credible intervals from 1000 draws of the joint posterior of the IFR by age curve. c Estimated demographic-vulnerability-weighted impact (DVWI), defined as the cumulative attack rate, spread uniformly by age, required to achieve a level of direct COVID-19 mortality matching the excess mortality in a assuming the posterior median IFR from b. Points and lines show median with 95% confidence intervals corresponding with 1000 draws from excess mortality estimates in a. All panels highlight in blue estimates for the WHO Africa region and Zambia for ease of identification.

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To help place these estimates in the context of differing underlying demography, we first weighted globally-derived estimates of age-specific IFR42 by population age-structure. According to this measure of demographic vulnerability to severe disease, Zambia ranked 2nd lowest in both Africa and the world, with only Uganda’s demographic structure producing a lower estimate (Fig. 2b). From these estimates, we then calculated a measure that standardises excess mortality by average protection from (or vulnerability to) severe disease upon infection that comes from population age structure. This measure, the “demographic vulnerability-weighted impact” (DVWI), is defined explicitly as the cumulative attack rate required to match estimates of excess mortality, assuming direct COVID-19 causation and age-specific IFR from Brazeau et al.42, with even spread of infection by age within the population. It is important to note that indirect pandemic consequences also impact excess mortality. Moreover, our estimates are based upon infection-fatality patterns during the pandemic’s first wave, largely using data from high-income settings with good care access and standards, relative to global averages. Consequently, this measure is not designed to provide insight into excess mortality causes, but places such estimates within the context of the population’s vulnerability to direct infection consequences at the beginning of the pandemic. It is, therefore, plausible that countries can have DVWI>1 due to any combination of: (i) high indirect pandemic impact; (ii) greater disease severity, due to health-care limitations, SARS-CoV-2 variants of greater severity or any other factor not accounted for which contribute to higher IFRs by age than those used in our analysis; (iii) substantial burden associated with reinfection.

Our estimates (Fig. 2c) highlight that, once demographic effects are removed, uncertainty in existing WHO estimates for Zambia (DVWI = 0.251, 95% CI: 0–0.710) permit very few conclusions about the differential impact of the disease relative to global patterns. We find that estimates for Zambia are no longer comparatively lower than the worst impacted Africa region countries such as South Africa (DVWI = 0.294, 95% CI: 0.265–0.320) or Algeria (DVWI = 0.417, 95% CI: 0.397–0.440), nor lower than the worst impacted WHO regions such as Europe (DVWI = 0.171, 95% CI: 0.167–0.176) and the Americas (DVWI = 0.240, 95% CI: 0.233–0.248).

Burial registration patterns in Lusaka

Fig. 3: Burial registrations and COVID-19 mortality patterns.

a Confirmed COVID-19 deaths in Lusaka Province, b total weekly burial registrations in Lusaka with a 5-week rolling average of the two preceding, current and two succeeding weeks, c age-grouped registrations relative to 2018–2019 mean, d weekly average age at death of burial registrations with similar 5-week rolling average, e age-grouped proportion of deaths in burial registrations. Dates of key non-pharmaceutical intervention (NPI) changes are also given (vertical dashed lines, 17th March 2020: initial COVID-19 press briefing and NPIs45, 24th April 2020: initial relaxation of some NPIs48, 6th June 2020: opening of primary and secondary schools for examination students only49, 10th October 2020: business restrictions fully lifted, COVID guidance continues, e.g., mask wearing, good hygiene etc57,85).

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Although burial registration is legally required in Zambia, deaths are not registered comprehensively, particularly when a substantial proportion occur in the community53,54. Overall, 14,665 and 14,992 Lusakan deaths were registered in 2018 and 2019, respectively, representing registration rates of 5.74 in 2018 and 5.85 in 2019 per 1000 population. United Nations Population Division projections (based on census and population survey data) of 6.7 and 6.6 Zambian deaths per 1000 in 2018 and 201955 suggest these registration rates could constitute 85.7% and 88.6% of total Lusaka deaths respectively. From a median of 302 registrations per week, these data show substantial pre-pandemic volatility (Fig. 3b), exhibiting a range exceeding 500 twice and falling below 100 six times during 2018–2019 weeks. Such variation seems unlikely to represent underlying mortality patterns when non-communicable disease, cancer and HIV/AIDs, accounting for approximately two thirds of Zambian deaths56, are unlikely to be subject to sharp, temporary declines. When grouped by age and plotted relative to their age-respective 2018-2019 averages (Fig. 3c), these data showed high consistency in pre-pandemic volatility across age-groups. This occurs even as underlying pre-pandemic causes of Zambian mortality vary substantially between age groups. For example, in children under the age of 5 years (U5), maternal and neonatal disorders are the primary cause of deaths (31.2%) compared to <1% in children aged 5–14; HIV/AIDS is the leading cause of death in children aged 5-14 representing 23.2% of deaths (compared to 13.8% of U5 deaths); injuries represent 14.6% of deaths in 5–14-year-olds but just 2.5% of U5 deaths56. In contrast to the weekly registration variability, the pre-pandemic average age at death and age-distribution of burial registrations by date of death are far more stable (Fig. 3d, e), with a median weekly average age of death of 37.6 years (95% CI: 33.9–41.4 years) during 2018–2019. Together, these data suggest that burial registration volatility is caused by service disruption, rather than inherent changes in underlying mortality.

When burial registrations were disaggregated by sex, males were disproportionately represented throughout the time-period (consistent with higher male mortality in Zambia57), but essentially no gendered differences are seen in relative registration changes throughout the pandemic compared with the pre-pandemic median (Supplementary Fig. 1).

Estimating excess mortality in Lusaka during 2020 to mid-2021

The inherent burial registration data volatility limits the utility of basing any predictive model of age-specific mortality trends on the absolute numbers of burial registrations at a given timepoint (Fig. 4a). Instead, we developed a statistical model that attempted to base such predictions on the age-distribution of deaths within those registered for burial. We cross-validated this model, showing that it could generate accurate predictions of the age-distribution of registrations during 2018–2019, stratified by 5-year age groups, (see Supplementary Methods and Supplementary Fig. 2). We then used it to generate predictions, based upon the total weekly U5 burial registrations, of the expected numbers of weekly burial registrations in all other age groups (5+) during January 2020–June 2021 (Fig. 4b). These predictions were compared with the observed data to estimate the excess number of 5+ burial registrations (Fig. 4c). To account for potential differential changes in U5 registration rates (i.e., if neonatal death registrations were differentially affected compared with older age groups), we repeated this process using registration rates from the 5 to 14 year age group to obtain a supplementary set of predictions.

Fig. 4: Excess mortality in Lusaka.

Figure shows a burial registrations grouped by age with 2018–2019 median shown throughout (dotted line), b burial registrations (points) with model fit (line and ribbon) combining age-distribution of deaths with the number of registrations aged <5 fitted to 2018–2019 burial registrations and predictions of expected registration in 2020–2021, c excess burial registrations per thousand people based on the difference between total burial registrations and 2020-21 model predictions, d scaling factor based on burial registrations aged <5 relative to their pre-pandemic median, e application of scaling factor to excess burial registration of population aged 5+ (black) to estimate median excess mortality (blue) and with additional assumption of 90% and 80% registration capture of underlying mortality (blue, dashed and dotted lines), f cumulative estimates of excess burial registrations (black), median cumulative mortality assuming weekly scaling (blue), and median cumulative mortality assuming 90% and 80% registration capture of underlying mortality (blue, dashed and dotted), g demographic-vulnerability- weighted index (DVWI, the cumulative attack rate required to achieve excess burial registrations (black), or mortality assuming scaling (blue) with 100%, 90%, and 80% registration capture of underlying mortality registration (solid, dashed and dotted) in e, f or in World Health Organisation (WHO) excess mortality estimates for Zambia, assuming the overall IFR for Lusakan and Zambian population structures, respectively, and direct COVID-19 causation. In all model plots, lines and ribbons show the median and 95% credible interval. Registrations, mortality and DVWI are grouped by week in all panels.

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Table 1 Excess burial registrations and mortality estimates in Lusaka

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We then applied a weekly scaling factor, equivalent to the relative difference between reported U5 weekly mortality and the median U5 registration rate during 2018–2019, to these estimates to account for our posited most plausible assumption (i.e., temporary changes in burial registration are primarily driven by registration process factors, rather than underlying changes in non-COVID-19 deaths) (Fig. 4d, e). This scaling produced cumulative estimated excess deaths of 2898 (95% CrI: 2031–3953) during 2020 and 3977 (95% CrI: 2931–5205) during 2020–June 2021 within the pre-pandemic proportion of the Lusakan population whose deaths would typically be registered (Fig. 4f).

These numbers therefore correspond to the most optimistic registration system assumptions (i.e., a 100% pre-pandemic registration rate), yielding 1551.5 (95% CrI: 1090.0–2097.2) deaths per million Lusakans throughout the study period. Though accurately quantifying this system coverage, herein referred to as ‘capture rate’, is impossible with available data, we make a baseline assumption, using local knowledge and census-based total mortality projections in Zambia, that approximately 90% of pre-pandemic deaths were registered (Fig. 4e, f). With this assumption, we estimate a total excess mortality of 3220 (95% CrI: 2256–4393) during 2020 and 4419 (95% CrI: 3257–5783) during 2020–June 2021, corresponding to 1256.2 (95% CrI: 880.1–1713.8) and 1723.9 (95% CrI: 1270.6–2256.0) deaths per million total population, respectively. A more pessimistic assumption of burial registration population coverage yields correspondingly more pessimistic excess mortality estimates (e.g., assuming 80% coverage produces an estimated 4971 (95% CrI: 3664–6506) excess deaths throughout the study period, Table 1). Our excess death estimates represented 18.5% (95% CrI: 13.0–25.2%) and 17.6% (95% CrI: 13.0–23.0%) of pre-pandemic burial registrations for the two respective time-periods, exceeding 50% of 2018–2019 median registrations (Supplementary Fig. 4, approaching 150% when filtered to deaths over 50 years) during the peaks of all three waves, are robust to this registration coverage uncertainty. Finally, we used these estimates to calculate DVWI values, finding that all these values exceeded comparative values based on WHO excess mortality estimates, with median excess mortality estimates (i.e., using the scaling factor) over twice as large as comparative WHO median for any capture rate (Fig. 4g).

SARS-CoV-2 transmission and COVID-19 severity during the first wave

Fig. 5: SARS-CoV-2 transmission in June–October 2020.

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We then assessed the model goodness of fit under differing age-gradient and overall COVID-19 IFR assumptions during Lusaka’s first epidemic wave (Fig. 6a, b). The log average posterior model fit to these data proved more sensitive to the IFR age-gradient than the overall Lusaka population-level IFR assumed, but centred largely upon estimates similar to our default model assumptions (Fig. 6c–e), with anything beyond a halving or doubling of either parameter producing quantitatively and qualitatively worse fits to the data (Supplementary Fig. 6). Where Zambia’s demography-weighted IFR under default assumptions is 0.11%, this range (80–167% of default overall IFR assumptions) corresponds with an overall population IFR between 0.088 and 0.183%.

Fig. 6: Inference of age-gradient and scale of severity.

a, b are infographics to show how the infection fatality ratio (IFR) curve changes when the intercept or slope is altered on a standard and b log scales. Each plot shows the default IFR from Brazeau et al.42 as a solid line, with relative overall severities of 20% and 500% of those default values or relative age-gradient of 20% and 250% of the slope on the log scale, maintaining the overall severity of the default. The heatmap c shows the log of the average posterior model fit over 100 samples. d, e show all assessed IFR curves, coloured by posterior fit as found in c, and where default IFR assumptions are highlighted in black, plotted on d standard and e log scales.

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We tested our assumptions through several sensitivity analyses (Supplementary Table 1, Supplementary Fig. 7). These included verifying that our results were not sensitive to the unexpectedly high post-mortem prevalence during 13th–19th July, 2020, compared with preceding and succeeding weekly prevalence (Supplementary Fig. 7b). Our default fits, which assume near-zero COVID-19 U5 mortality, produced lower-than-observed U5 mortuary prevalence; a sensitivity analysis excluding U5 mortuary prevalence demonstrated fits favouring marginally higher IFRs in the remaining 5+ population (Supplementary Fig. 7c). To assess the sensitivity of our results to our assumptions around declines in overall burial registration rates, we also refitted our model to our excess mortality estimates without applying the scaling factors used in our default approach and censoring data during acute burial registration service disruption during 6th–19th July, 2020. We found this approach favoured marginally higher IFRs than our default set of assumptions (Supplementary Fig. 7d).

We found limited sensitivity of our results to duration between infection and death, though an increase in duration again favoured marginally higher IFRs (Supplementary Fig. 7e, f). Furthermore, we conducted a sensitivity analysis of our underlying assumption that the pre-pandemic relative rates of 5+ baseline non-COVID-19 mortality remained consistent with U5 mortality during the pandemic. We did this by varying the rates of estimated pre-pandemic non-COVID-19-related mortality in 5+ relative to U5s by ±10% (e.g., either a differential reduction in U5 mortality greater than would occur if all deaths due to respiratory illness in U5 fell to zero or a differential reduction in 5+ mortality greater than all deaths from injuries in 5+ falling to zero56) and by ±20% (e.g., either a differential reduction in U5 mortality greater than would occur if all deaths due to respiratory and diarrhoeal illness or a differential reduction in 5+ mortality greater than deaths from injuries and HIV falling to zero56).We found that a 10% rate increase or decrease produced only nuanced impact upon the distribution of the fit to different IFR patterns, with our default parameters remaining well within the envelope of best-fitting values (Supplementary Fig. 7g, h). Meanwhile, a 20% decrease in relative non-COVID-19 5+ mortality produced substantially higher IFR estimates, excluding our default IFR assumptions and including a fit with flatter IFR age-gradient (Supplementary Fig. 7i). A 20% increase produced lower estimates (although still solidly encompassing our default IFR patterns) and included produced best-fitting scenario with a steeper IFR age-gradient (Supplementary Fig. 7j). Finally, we varied our 90% burial registration capture rate assumption, again finding nuanced differences, with a small increase in severity with 80% burial registration capture (Supplementary Fig. 7k–l).

Within the best-fitting-model envelope (Supplementary Fig. 8), infection spread and transmissibility estimates are similar to those of the default model (Fig. 4), suggesting a reproduction number well above two during May-June 2020 but falling progressively to below one by late July 2020 as the epidemic peaked. Our estimates suggest that this transmission decline occurred despite population-level immunity remaining low, with our cumulative attack rate estimates (the % of the population infected at any point during the wave) ranging between 15 and 30% during the first wave.

Discussion

Uncertainty in mortality patterns in Africa throughout the pandemic is one of the leading contributors to the remaining uncertainty in the impact of the pandemic globally33,59,60,61. While the findings presented here are not readily extrapolated to a wider region due to substantial heterogeneity between countries33, our COVID-19 impact estimates in Lusaka represent a substantial narrowing of uncertainty in one of the many countries in Africa where current estimates of impact are largely uninformative and where age demographics are amongst the most favourable globally in terms of reducing the average likelihood of severe disease upon infection.

Our results strongly suggest that the first COVID-19 wave in Lusaka had a direct and heavy impact, shifting the age-distribution of mortality towards older ages in a manner highly characteristic of COVID-19 severity patterns observed elsewhere. Assuming U5 burial registration declines represent registration process rather than underlying mortality changes, our estimates of 3220 (95% CrI: 2256–4393) excess deaths in 2020 represent a per-capita rate of 1256.2 (95% CrI: 880.1–1713.8) excess deaths per million, approaching the highest affected Africa-region countries (South Africa: 920 (95% CI: 830–1000) per million, and Algeria: 1270 (95% CI: 1210–1340) per million). After accounting for the protective effect of Lusaka’s young population, these estimates far exceeded those measured for Europe and the Americas (DVWI: 1.149 (95% CrI: 0.805–1.568) compared with 0.171 (95% CI: 0.167–0.176) and 0.240 (95% CI: 0.233–0.248), respectively). Assuming, instead, that registration declines are driven by underlying mortality changes decreased our excess 2020 mortality estimates by 48.7%, although estimates accounting for Lusaka’s population demographic remained well above those in the aforementioned countries (DVWI: 0.589 (95% CrI: 0.431–0.742)). In contrast, our default estimates may be conservative if, for example, disruptions to burial registrations were mirrored by, and subsequently mask any impact thereof, any disruption to maternal and neonatal services62. U5 registration patterns, however, correlated well with older children and younger adults (despite having different typical mortality causes) and were highly predictive of trends across all age groups when NPIs were implemented, with similar results using the 5–14 year age group as a reference category, suggesting that mortality changes due to behavioural factors may have been relatively nuanced in the short term.

Using a transmission model parameterised by IFR patterns estimated from available global 2020 data42, we show that these mortality patterns correspond well to community and post-mortem mortuary prevalence data, with these IFR patterns well within the range of our best-fitting models. Consequently, we find no evidence that age-specific severity was markedly different from estimates in other geographies, or any support for a so-called “Africa paradox”12,16,17. Our results do not, however, preclude relatively nuanced IFR differences in Lusaka relative to those observed elsewhere, with our results showing a plausible relative severity range between 50 and 250% of our default assumptions across numerous sensitivity analyses. Indeed, given the high non-hospital death prevalence27, we might expect to see greater severity in Lusaka compared with estimates from settings with high hospital access under the same intrinsic disease pathogenicity assumptions. IFR patterns toward our uncertainty interval’s higher end would also bring our estimates more in line with others from low- and middle-income settings outside of Africa29,36. Alternatively, IFR patterns toward the interval’s lower end could suggest relatively nuanced intrinsic severity differences in Lusaka that remain unexplained. However, given the widespread low hospital-care access across large parts of Africa, such an observation would not support a low direct pandemic impact across the continent. Meanwhile, COVID-19 treatment advances throughout 202063, largely benefitting high-income-country patients, are likely to mean that, even at the most optimistic end of our uncertainty, prioritising prevention efforts in higher-income-setting individuals over equivalently-aged Lusakans could have no equitable justification.

Our default IFR assumptions are based on results from Brazeau et al.42, calculated using data matching our main study period time-frame (i.e., prior to the emergence of new variants of possible differing severity). As with all severity studies during the initial pandemic stages, this study included data representing a trade-off between study-design quality and the representation of a wide range of contexts, leading to data inclusion with some potential measurement error. Bias may therefore be present in some population exposure data (i.e., data collected through convenience sampling including shopping centre attendees and blood donors) and COVID-19 mortality data (i.e., where confirmed COVID-19 mortality use can underestimate total attributable mortality). However, the included data come from countries with strong testing systems, and crude IFR estimates from convenience-sample sources are not dissimilar to other included estimates. Other studies have suggested a higher IFR for very young children relative to older children64,65,66, which might account for the high observed U5 post-mortem prevalence, though at levels (<0.01%) that would make negligible difference to the fit of our model to the data. A plausible explanation for this U5 prevalence, though not one quantifiable in our framework, could be comparatively extensive SARS-CoV-2 spread within communities of high non-COVID-19-associated infant mortality. Overall, Brazeau et al. IFR estimates are central within the range of other estimates42. Thus, it seems plausible that an ensemble approach could broaden our uncertainty but would be unlikely to alter our central conclusion that, when analysing one of the best-characterised epidemics in sub-Saharan Africa, there is no evidence to support any substantial differences between innate COVID-19 severity in Lusaka relative to estimates from other parts of the world.

Unfortunately, our estimates suggest that by the year’s end most of the population remained entirely immune-naïve, suggesting that Lusakans remained highly vulnerable to future waves, even in the absence of the variants that subsequently emerged. Our estimates of subsequent peaks in excess mortality suggest that many gains achieved by control measures during 2020, similarly implemented in many African countries74, were lost later in 2020 and 2021. Given vaccine availability delays in Zambia throughout 202175,76, maintaining these early gains would have involved maintaining costly suppression measures well beyond the duration they needed to be maintained in higher-income countries where vaccine distribution was prioritised. The unsubstantiated perception of a low COVID-19 severity across Africa seems unlikely to have helped advocacy for equitable vaccine access, nor combat vaccine hesitancy in many African countries when vaccines were introduced77,78. In this context, a global inequity in our ability to measure mortality patterns and disease spread in 2020 likely contributed substantially to global inequity in the impact of the pandemic.

Methods

WHO estimates of excess mortality in Zambia in 2020

Estimates of excess mortality during 2020 were sourced from the WHO33 and plotted per million population with 95% confidence intervals at global and WHO-region level, and at national level within the Africa WHO region. Confirmed WHO COVID-19 mortality data for countries and regions are plotted alongside for ref. 2.

Population-weighted country and region specific overall IFR were calculated and plotted using 100,000 samples from the age-specific IFR distribution, derived by Brazeau et al.42 (without seroreversion), which we weighted by the specified population structure and summed to generate an overall value with 95% credible intervals. Population demographic estimates were sourced from the UN World Population Prospects79 and grouped into 5-year age brackets, then summarised at the global, regional and country level (consistent with excess mortality estimates).

DVWI was defined as the cumulative population attack rate required to directly generate the estimated excess mortality impact, given the median global, region or country-specific overall IFR and assuming an even spread of infection by age (for negative values of excess mortality this DVWI was set to 0). We calculated these DVWI values by dividing estimates of excess mortality by median overall area-specific IFR estimates to generate a population infection level reflective of area demography and mortality.

Model framework and fitting

Estimating excess mortality in Lusaka using burial registration data

Official reported deaths for Lusaka Province were obtained from the Zambia COVID-19 Dashboard43, while details of NPIs were obtained from situation reports from the Zambia National Public Health Institute website80 and governmental statements on the COVID-19 pandemic44,45,46,47,48,49,50,51. Calculations involving Lusaka population size and demography were obtained from the Zambia Statistics Agency81, which are recently updated census-based projections.

Modelling SARS-CoV-2 transmission

SARS-CoV-2 transmission in Lusaka’s first wave of the pandemic (June-October 2020) was modelled using an age-structured SARS-CoV-2 SEIR model58 (Supplementary Figure 10) with age-specific population estimates for Lusaka District obtained from the Zambia Statistics Agency81. In the absence of locally collected data on social contact patterns we used a social contact matrix generated from data collected in Manicaland, Zimbabwe, the nearest geographical location in the literature with a matrix that describes contacts across all ages of interest, filtering to only include data from the peri-urban region (Nyanga) within the dataset83. As is generally the case in data collected from lower-income countries84, this matrix produces attack rates throughout an epidemic which are much flatter by age then any equivalent simulation using data from higher-income settings. For validation, PCR prevalence and seroprevalence patterns by age as observed in the population-based survey in Lusaka were compared with those estimated contemporaneously by the model. We also adjusted standard parameterisation to account for some uncertainties in treatment access in Lusaka (see Supplementary Methods, Supplementary Table 2).

Inferring COVID-19 severity in Lusaka

Additional details of methods are given in Supplementary Information.

Ethics and inclusion statement

We are not aware of any restrictions or prohibitions that have applied to the work of local researchers in this analysis.

Ethical oversight for ZPRIME and the COVID-19 expansion that generated post-mortem PCR prevalence data from UTH27 were provided by the institutional review boards at Boston RESEARCH University and the University of Zambia. Written informed consent was obtained from the deceased’s family members or representatives.

The population level SARS-CoV-2 prevalence study37 was approved by the Zambia National Health Research Authority and the University of Zambia Biomedical Research Ethics Committee. The study was reviewed in accordance with the Centers for Disease Control and Prevention (CDC) human research protection procedures and was determined to be non-research. Written informed consent was obtained for adults (aged ≥18 years) and emancipated minors, parental consent was obtained for participants aged 17 years and younger, and assent was obtained for participants aged 7–17 years, before the study.

The work was granted approval via Imperial’s Research Governance Integrity framework on the basis of the above pre-existing ethics approvals.

We are not aware of any personal risks to participants, but we are eternally grateful to them. In particular, we are grateful to the families who consented to the collection of post-mortem samples from loved ones during some of the hardest times imaginable and to these data being used within secondary analyses such as ours.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Code availability

All the included data and code with instructions for the reproduction of these results can be found at https://github.com/RJSheppard/COVID.IFR.Lusaka (https://doi.org/10.5281/zenodo.7963552).

Acknowledgements

This work was supported by funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1: R.S., O.J.W., G.B., I.C.G.G., D.O.M., C.W., S.G., L.C.O., A.C.G., P.G.T.W.), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement, and is also part of the EDCTP2 programme supported by the European Union and funding by Community Jameel. The research has also been supported in part by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the Centers for Disease Control and Prevention (CDC) through a cooperative agreement with the Zambia Ministry of Health: buffer component COVID-19 response funding No: CDC-FRA-GH15-160005CONT19, “Strengthening the Zambian Ministry of Health’s capacity to provide Leadership to the National COVID-19 Response through Coordination, Policy Development, Mentorship and Training under the International Task Force” (Cooperative Agreement number: 5 NU2GGH001617-05): J.Z.H., L.B.M. The ZPRIME study, COVID-19 expansion and further support of these analyses were funded by the Bill & Melinda Gates Foundation (OPP 1163027: R.P., J.L., G.K., C.M., W.B.M., L.M., C.J.G.). Additional funders included The Wellcome Trust and the UK Foreign, Commonwealth & Development Office (FCDO) (reference 221350/Z/20/Z: P.G.W., O.J.W.), an Academy of Medical Sciences Springboard Fellowship (reference SBF005\1107: P.G.W., R.S.), a Schmidt Science Fellowship in partnership with the Rhodes Trust and the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services (HHS) as part of financial assistance award (reference U01GH002319, OJW), the European Research Council (ERC) under Horizon 2020 research and innovation programme (Grant agreement No. 101003183: A.M.) with additional funding from the Fondazione Romeo & Enrica Invernizzi to the Bocconi Covid Crisis Lab, and a Sir Henry Wellcome Postdoctoral Fellowship (reference 224190/Z/21/Z: C.W.). We are keenly aware of the personal cost of generating the data used in this analysis incurred by both the ZPRIME and ZNPHI teams and would like to acknowledge all researchers and staff who contributed to the collection of data used in these analyses. In particular, we pay tribute to Roy Chavuma, a key public health expert within the team who was lost to COVID-19 in the course of the original mortuary study. We also acknowledge Philip Whiteside, whose journalism during the pandemic, which aimed to provide the public with a better understanding of the global impact of COVID-19, helped to facilitate the early stages of the collaboration underpinning this analysis. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the funding agencies or the agencies that the authors are from.

Author information

These authors contributed equally: Patrick G. T. Walker, Lawrence Mwananyanda, Christopher J. Gill.

Authors and Affiliations

MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, UK

Richard J. Sheppard, Oliver J. Watson, Gregory Barnsley, Nicholas F. Brazeau, Ines C. G. Gerard-Ursin, Daniela Olivera Mesa, Charles Whittaker, Simon Gregson, Lucy C. Okell, Azra C. Ghani & Patrick G. T. Walker

Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK

Oliver J. Watson

Department of Global Health, Boston University School of Public Health, Boston, MA, USA

Rachel Pieciak, William B. MacLeod, Lawrence Mwananyanda & Christopher J. Gill

Avencion Limited, Lusaka, Zambia

James Lungu, Crispin Moyo & Lawrence Mwananyanda

Department of Biomedical Sciences, School of Health Sciences, University of Zambia, Lusaka, Zambia
Zambia National Public Health Institute, Lusaka, Zambia

Stephen Longa Chanda

Manicaland Centre for Public Health Research, Biomedical Research and Training Institute, Harare, Zimbabwe
Carlo F. Dondena Centre for Research on Social Dynamics and Public Policy, Boccini University, Milan, Italy

Emanuele Del Fava & Alessia Melegaro

Max Planck Institute for Demographic Research, Rostock, Germany

Emanuele Del Fava

Department of Social and Political Science, Bocconi University, Milano, Italy
Centers for Disease Control and Prevention, Lusaka, Zambia

Jonas Z. Hines

Zambia Ministry of Health, Lusaka, Zambia

Lloyd B. Mulenga

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Corresponding author

Correspondence toPatrick G. T. Walker.

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Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Sheppard, R.J., Watson, O.J., Pieciak, R. et al. Using mortuary and burial data to place COVID-19 in Lusaka, Zambia within a global context.Nat Commun 14, 3840 (2023). https://doi.org/10.1038/s41467-023-39288-6

Received13 January 2023
Accepted06 June 2023

Коронавирус в Замбии

последнее обновление: 25.02.2024 05:16

Текущая статистика по коронавирусу на 25.02.2024

| Население | 19 470 тыс. | |

| ——————- | ————— | —— |

| Всего заражений | 349 304 | 1,8 |

| Смертельные случаи | 4 069 | 1,2 % |

| Выздоровевшие | 341 316 | 97,7 % |

| Сейчас болеют | 3 919 | 1,1 % |

| Сделано тестов | 4 112 961 | |

| тестов на 1 млн. | 211 244 | |

Статистика по месяцам

Статистика за февраль 2024

| ————- | ————— | —————— | ————— | ——— |

| на 01.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 02.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 03.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 04.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 05.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 06.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 07.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 08.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 09.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 10.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 11.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 12.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 13.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 14.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 15.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 16.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 17.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 18.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 19.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 20.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 21.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 22.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 23.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 24.02.2024 | 349304 | 4069 | 341316 | 3919 |

| на 25.02.2024 | 349304 | 4069 | 341316 | 3919 |

умершие выздоровевшие болеющие Всего 05.02.202412.02.202419.02.202425.02.2024 50000100000150000200000250000300000350000400000 умершиевыздоровевшие болеющие

Статистика за февраль 2024(количество по дням)

| ————- | ————— | —————— | ————— |

| 01.02.2024 Чт | | | |

| 02.02.2024 Пт | | | |

| 03.02.2024 Сб | | | |

| 04.02.2024 Вс | | | |

| 05.02.2024 Пн | | | |

| 06.02.2024 Вт | | | |

| 07.02.2024 Ср | | | |

| 08.02.2024 Чт | | | |

| 09.02.2024 Пт | | | |

| 10.02.2024 Сб | | | |

| 11.02.2024 Вс | | | |

| 12.02.2024 Пн | | | |

| 13.02.2024 Вт | | | |

| 14.02.2024 Ср | | | |

| 15.02.2024 Чт | | | |

| 16.02.2024 Пт | | | |

| 17.02.2024 Сб | | | |

| 18.02.2024 Вс | | | |

| 19.02.2024 Пн | | | |

| 20.02.2024 Вт | | | |

| 21.02.2024 Ср | | | |

| 22.02.2024 Чт | | | |

| 23.02.2024 Пт | | | |

| 24.02.2024 Сб | | | |

| 25.02.2024 Вс | | | |

| Всего | 0 | 0 | 0 |

Статистика развития пандемии коронавируса Covid-19 в Замбии

На 25 февраля 2024 в Замбии зафиксировано 320 680 случаев заражения коронавирусом Covid-19. За последние сутки число зараженных выросло на 89 человек.

Общее число смертей от коронавирусной инфекции в Замбии составляет 3 983 человека, сегодня зафиксировано 0 случаев смерти.

В активной фазе болезни находятся 558 человек, из них 1 в критическом состоянии. Уровень летальности: 1.24%.

Подтвержденных случаев полного излечения от вируса на сегодня, 25 февраля 2024 в Замбии: 316 139.

На графике представлены значения подтвержденных случаев заражения коронавирусом Covid-19 в Замбии по дням от начала сбора официальной статистической информации.

* Нулевые значения означают отсутствие данных