Excess mortality associated with the COVID-19 pandemic during the 2020 and 2021 waves in Antananarivo, Madagascar

INTRODUCTION

COVID-19-associated mortality remains difficult to estimate in sub-Saharan Africa because of the lack of comprehensive systems of death registration. Based on death registers referring to the capital city of Madagascar, we sought to estimate the excess mortality during the COVID-19 pandemic and calculate the loss of life expectancy.

METHODS

Death records between 2016 and 2021 were used to estimate weekly excess mortality during the pandemic period. To infer its synchrony with circulation of SARS-CoV-2, a cross-wavelet analysis was performed. Life expectancy loss due to the COVID-19 pandemic was calculated by projecting mortality rates using the Lee and Carter model and extrapolating the prepandemic trends (1990-2019). Differences in life expectancy at birth were disaggregated by cause of death.

RESULTS

Peaks of excess mortality in 2020-21 were associated with waves of COVID-19. Estimates of all-cause excess mortality were 38.5 and 64.9 per 100 000 inhabitants in 2020 and 2021, respectively, with excess mortality reaching ≥50% over 6 weeks. In 2021, we quantified a drop of 0.8 and 1.0 years in the life expectancy for men and women, respectively attributable to increased risks of death beyond the age of 60 years.

CONCLUSION

We observed high excess mortality during the pandemic period, in particular around the peaks of SARS-CoV-2 circulation in Antananarivo. Our study highlights the need to implement death registration systems in low-income countries to document true toll of a pandemic.

Impact of health system strengthening on delivery strategies to improve child immunisation coverage and inequalities in rural Madagascar

Background

To reach global immunisation goals, national programmes need to balance routine immunisation at health facilities with vaccination campaigns and other outreach activities (eg, vaccination weeks), which boost coverage at particular times and help reduce geographical inequalities. However, where routine immunisation is weak, an over-reliance on vaccination campaigns may lead to heterogeneous coverage. Here, we assessed the impact of a health system strengthening (HSS) intervention on the relative contribution of routine immunisation and outreach activities to reach immunisation goals in rural Madagascar.

Methods

We obtained data from health centres in Ifanadiana district on the monthly number of recommended vaccines (BCG, measles, diphtheria, tetanus and pertussis (DTP) and polio) delivered to children, during 2014–2018. We also analysed data from a district-representative cohort carried out every 2 years in over 1500 households in 2014–2018. We compared changes inside and outside the HSS catchment in the delivery of recommended vaccines, population-level vaccination coverage, geographical and economic inequalities in coverage, and timeliness of vaccination. The impact of HSS was quantified via mixed-effects logistic regressions.

Results

The HSS intervention was associated with a significant increase in immunisation rates (OR between 1.22 for measles and 1.49 for DTP), which diminished over time. Outreach activities were associated with a doubling in immunisation rates, but their effect was smaller in the HSS catchment. Analysis of cohort data revealed that HSS was associated with higher vaccination coverage (OR between 1.18 per year of HSS for measles and 1.43 for BCG), a reduction in economic inequality, and a higher proportion of timely vaccinations. Yet, the lower contribution of outreach activities in the HSS catchment was associated with persistent inequalities in geographical coverage, which prevented achieving international coverage targets.

Conclusion

Investment in stronger primary care systems can improve vaccination coverage, reduce inequalities and improve the timeliness of vaccination via increases in routine immunisations.

Tensor decomposition for infectious disease incidence data

Many demographic and ecological processes generate seasonal and other periodicities. Seasonality in infectious disease transmission can result from climatic forces such as temperature and humidity; variation in contact rates as a result of migration or school calendar; or temporary surges in birth rates. Seasonal drivers of acute immunizing infections can also drive longer-term fluctuations.Tensor decomposition has been used in many disciplines to uncover dominant trends in multi-dimensional data. We introduce tensors as a novel method for decomposing oscillatory infectious disease time series.We illustrate the reliability of the method by applying it to simulated data. We then present decompositions of measles data from England and Wales. This paper leverages simulations as well as much-studied data to illustrate the power of tensor decomposition to uncover dominant epidemic signals as well as variation in space and time. We then use tensor decomposition to uncover new findings and demonstrate the potential power of the method for disease incidence data. In particular, we are able to distinguish between annual and biennial signals across locations and shifts in these signals over time.Tensor decomposition is able to isolate variation in disease seasonality as a result of variation in demographic rates. The method allows us to discern variation in the strength of such signals by space and population size. Tensors provide an opportunity for a concise approach to uncovering heterogeneity in disease transmission across space and time in large datasets.

Structure, space and size: competing drivers of variation in urban and rural measles transmission

A key concern in public health is whether disparities exist between urban and rural areas. One dimension of potential variation is the transmission of infectious diseases. In addition to potential differences between urban and rural local dynamics, the question of whether urban and rural areas participate equally in national dynamics remains unanswered. Specifically, urban and rural areas may diverge in local transmission as well as spatial connectivity, and thus risk for receiving imported cases. Finally, the potential confounding relationship of spatial proximity with size and urban/rural district type has not been addressed by previous research. It is rare to have sufficient data to explore these questions thoroughly. We use exhaustive weekly case reports of measles in 954 urban and 468 rural districts of the UK (1944–1965) to compare both local disease dynamics as well as regional transmission. We employ the time-series susceptible–infected–recovered model to estimate disease transmission, epidemic severity, seasonality and import dependence. Congruent with past results, we observe a clear dependence on population size for the majority of these measures. We use a matched-pair strategy to compare proximate urban and rural districts and control for possible spatial confounders. This analytical strategy reveals a modest difference between urban and rural areas. Rural areas tend to be characterized by more frequent, smaller outbreaks compared to urban counterparts. The magnitude of the difference is slight and the results primarily reinforce the importance of population size, both in terms of local and regional transmission. In sum, urban and rural areas demonstrate remarkable epidemiological similarity in this recent UK context.

Vaccine-driven virulence evolution: consequences of unbalanced reductions in mortality and transmission and implications for pertussis vaccines

Many vaccines have heterogeneous effects across individuals. Additionally, some vaccines do not prevent infection, but reduce disease-associated mortality and transmission. Both of these factors will alter selection pressures on pathogens and thus shape the evolution of pathogen virulence. We use a mathematical modelling framework to show that (i) the balance of how vaccines reduce transmission versus mortality and (ii) individual variability in protection conferred both shape the evolution of pathogen virulence. Epidemiological (burden of disease) and evolutionary (pathogen virulence) outcomes are both worse when vaccines confer smaller reductions in transmission than in mortality. Furthermore, outcomes are modulated by variability in vaccine effects, with increased variability limiting the extent of virulence evolution but in some cases preventing eradication. These findings are pertinent to current concerns about the global resurgence of pertussis and the efficacy of pertussis vaccines, as the two classes of these vaccines may reduce disease symptoms without preventing infection and differ in their ability to reduce transmission. Furthermore, these findings point to the importance of generating precise predictions for virulence evolution in Bordetella pertussis (and other similar pathogens) by incorporating empirical characterizations of vaccine effects into models capturing the epidemiological details of this system.

Disentangling serology to elucidate henipa- and filovirus transmission in Madagascar fruit bats

Bats are reservoirs for emerging human pathogens, including Hendra and Nipah henipaviruses and Ebola and Marburg filoviruses. These viruses demonstrate predictable patterns in seasonality and age structure across multiple systems; previous work suggests that they may circulate in Madagascar's endemic fruit bats, which are widely consumed as human food. We aimed to (a) document the extent of henipa- and filovirus exposure among Malagasy fruit bats, (b) explore seasonality in seroprevalence and serostatus in these bat populations and (c) compare mechanistic hypotheses for possible transmission dynamics underlying these data. To this end, we amassed and analysed a unique dataset documenting longitudinal serological henipa- and filovirus dynamics in three Madagascar fruit bat species. We uncovered serological evidence of exposure to Hendra-/Nipah-related henipaviruses in Eidolon dupreanum, Pteropus rufus and Rousettus madagascariensis, to Cedar-related henipaviruses in E. dupreanum and R. madagascariensis and to Ebola-related filoviruses in P. rufus and R. madagascariensis. We demonstrated significant seasonality in population-level seroprevalence and individual serostatus for multiple viruses across these species, linked to the female reproductive calendar. An age-structured subset of the data highlighted evidence of waning maternal antibodies in neonates, increasing seroprevalence in young and decreasing seroprevalence late in life. Comparison of mechanistic epidemiological models fit to these data offered support for transmission hypotheses permitting waning antibodies but retained immunity in adult-age bats. Our findings suggest that bats may seasonally modulate mechanisms of pathogen control, with consequences for population-level transmission. Additionally, we narrow the field of candidate transmission hypotheses by which bats are presumed to host and transmit potentially zoonotic viruses globally.

Population trends for two Malagasy fruit bats

Madagascar is home to three endemic species of Old World Fruit Bat, which are important pollinators and seed dispersers. We aimed to quantitatively assess population trajectories for the two largest of these species, the IUCN-listed 'Vulnerable' Eidolon dupreanum and Pteropus rufus. To this end, we conducted a longitudinal field study, in which we live-captured E. dupreanum and P. rufus, estimated species-specific fecundity rates, and generated age-frequency data via histological analysis of cementum annuli layering in tooth samples extracted from a subset of individuals. We fit exponential models to resulting data to estimate annual survival probabilities for adult bats (s _A = .794 for E. dupreanum; s _A = .511 for P. rufus), then applied Lefkovitch modeling techniques to infer the minimum required juvenile survival rate needed to permit longterm population persistence. Given estimated adult survival, population persistence was only possible for E. dupreanum when field-based fecundity estimates were replaced by higher values reported in the literature for related species. For P. rufus, tooth-derived estimates of adult survival were so low that even assumptions of perfect (100%) juvenile annual survival would not permit stable population trajectories. Age-based survival analyses were further supported by longitudinal exit counts carried out from 2013-2018 at three local P. rufus roost sites, which demonstrated a statistically significant, faintly negative time trend, indicative of subtle regional population declines. These results suggest that Malagasy fruit bat species face significant threats to population viability, with P. rufus particularly imperiled. Immediate conservation interventions, including habitat restoration and cessation of legally sanctioned bat hunting, are needed to protect Madagascar's fruit bats into the future.

Challenges and Opportunities in Disease Forecasting in Outbreak Settings: A Case Study of Measles in Lola Prefecture, Guinea

We report on and evaluate the process and findings of a real-time modeling exercise in response to an outbreak of measles in Lola prefecture, Guinea, in early 2015 in the wake of the Ebola crisis. Multiple statistical methods for the estimation of the size of the susceptible (i.e., unvaccinated) population were applied to weekly reported measles case data on seven subprefectures throughout Lola. Stochastic compartmental models were used to project future measles incidence in each subprefecture in both an initial and a follow-up iteration of forecasting. Measles susceptibility among 1- to 5-year-olds was estimated to be between 24% and 43% at the beginning of the outbreak. Based on this high baseline susceptibility, initial projections forecasted a large outbreak occurring over approximately 10 weeks and infecting 40 children per 1,000. Subsequent forecasts based on updated data mitigated this initial projection, but still predicted a significant outbreak. A catch-up vaccination campaign took place at the same time as this second forecast and measles cases quickly receded. Of note, case reports used to fit models changed significantly between forecast rounds. Model-based projections of both current population risk and future incidence can help in setting priorities and planning during an outbreak response. A swiftly changing situation on the ground, coupled with data uncertainties and the need to adjust standard analytical approaches to deal with sparse data, presents significant challenges. Appropriate presentation of results as planning scenarios, as well as presentations of uncertainty and two-way communication, is essential to the effective use of modeling studies in outbreak response.

Environmental drivers of the spatiotemporal dynamics of respiratory syncytial virus in the United States

Epidemics of respiratory syncytial virus (RSV) are known to occur in wintertime in temperate countries including the United States, but there is a limited understanding of the importance of climatic drivers in determining the seasonality of RSV. In the United States, RSV activity is highly spatially structured, with seasonal peaks beginning in Florida in November through December and ending in the upper Midwest in February-March, and prolonged disease activity in the southeastern US. Using data on both age-specific hospitalizations and laboratory reports of RSV in the US, and employing a combination of statistical and mechanistic epidemic modeling, we examined the association between environmental variables and state-specific measures of RSV seasonality. Temperature, vapor pressure, precipitation, and potential evapotranspiration (PET) were significantly associated with the timing of RSV activity across states in univariate exploratory analyses. The amplitude and timing of seasonality in the transmission rate was significantly correlated with seasonal fluctuations in PET, and negatively correlated with mean vapor pressure, minimum temperature, and precipitation. States with low mean vapor pressure and the largest seasonal variation in PET tended to experience biennial patterns of RSV activity, with alternating years of “early-big” and “late-small” epidemics. Our model for the transmission dynamics of RSV was able to replicate these biennial transitions at higher amplitudes of seasonality in the transmission rate. This successfully connects environmental drivers to the epidemic dynamics of RSV; however, it does not fully explain why RSV activity begins in Florida, one of the warmest states, when RSV is a winter-seasonal pathogen. Understanding and predicting the seasonality of RSV is essential in determining the optimal timing of immunoprophylaxis.

Respiratory syncytial virus (RSV) causes annual outbreaks of respiratory disease every winter in temperate climates, which can be severe particularly among infants. In the United States, RSV activity begins each autumn in Florida and appears to spread from the southeast to the northwest. Using data on hospitalizations and laboratory tests for RSV, we show that the timing of epidemics is associated with a variety of climatic factors, including temperature, vapor pressure, precipitation, and potential evapotranspiration (PET). Furthermore, using a dynamic model, we show that seasonal variation in the transmission rate of RSV can be correlated with the amplitude and timing of variation in PET, which is a measure of demand for water from the atmosphere. States with colder, drier weather and a large seasonal swing in PET tended to experience an alternating pattern of “early-big” RSV epidemics one year followed by a “late-small” epidemic the next year, which our model was able to reproduce based on the interaction between susceptible and infectious individuals. However, we cannot fully explain why epidemics begin in Florida. Being able to understand and predict the timing of RSV activity is important for optimizing the delivery of immunoprophylaxis to high-risk individuals.