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Modelling the effective reproduction number of vector-borne diseases: the yellow fever outbreak in Luanda, Angola 2015-2016 as an example.

ABSTRACT: The burden of vector-borne diseases (Dengue, Zika virus, yellow fever, etc.) gradually increased in the past decade across the globe. Mathematical modelling on infectious diseases helps to study the transmission dynamics of the pathogens. Theoretically, the diseases can be controlled and eventually eradicated by maintaining the effective reproduction number, ( Reff ), strictly less than 1. We established a vector-host compartmental model, and derived ( Reff ) for vector-borne diseases. The analytic form of the ( Reff ) was found to be the product of the basic reproduction number and the geometric average of the susceptibilities of the host and vector populations. The ( Reff ) formula was demonstrated to be consistent with the estimates of the 2015-2016 yellow fever outbreak in Luanda, and distinguished the second minor epidemic wave. For those using the compartmental model to study the vector-borne infectious disease epidemics, we further remark that it is important to be aware of whether one or two generations is considered for the transition "from host to vector to host" in reproduction number calculation.

SUBMITTER: Zhao S

PROVIDER: S-EPMC7049463 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Modelling the effective reproduction number of vector-borne diseases: the yellow fever outbreak in Luanda, Angola 2015-2016 as an example.

Zhao Shi S Musa Salihu S SS Hebert Jay T JT Cao Peihua P Ran Jinjun J Meng Jiayi J He Daihai D Qin Jing J

PeerJ 20200227

The burden of vector-borne diseases (Dengue, Zika virus, yellow fever, etc.) gradually increased in the past decade across the globe. Mathematical modelling on infectious diseases helps to study the transmission dynamics of the pathogens. Theoretically, the diseases can be controlled and eventually eradicated by maintaining the effective reproduction number, ( Reff ), strictly less than 1. We established a vector-host compartmental model, and derived ( Reff ) for vector-borne diseases. The analy ...[more]

PMID: 32149023

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Project description:Yellow fever (YF), transmitted via bites of infected mosquitoes, is a life-threatening viral disease endemic to tropical and subtropical regions of Africa and South America. YF has largely been controlled by widespread national vaccination campaigns. Nevertheless, between December 2015 and August 2016, YF resurged in Angola, quickly spread and became the largest YF outbreak for the last 30 years. Recently, YF resurged again in Brazil (December 2016). Thus, there is an urgent need to gain better understanding of the transmission pattern of YF.The present study provides a refined mathematical model, combined with modern likelihood-based statistical inference techniques, to assess and reconstruct important epidemiological processes underlying Angola's YF outbreak. This includes the outbreak's attack rate, the reproduction number ([Formula: see text]), the role of the mosquito vector, the influence of climatic factors, and the unusual but noticeable appearance of two-waves in the YF outbreak. The model explores actual and hypothetical vaccination strategies, and the impacts of possible human reactive behaviors (e.g., response to media precautions).While there were 73 deaths reported over the study period, the model indicates that the vaccination campaign saved 5.1-fold more people from death and saved from illness 5.6-fold of the observed 941 cases. Delaying the availability of the vaccines further would have greatly worsened the epidemic in terms of increased cases and deaths. The analysis estimated a mean [Formula: see text] and an attack rate of 0.09-0.15% (proportion of population infected) over the whole period from December 2015 to August 2016. Our estimated lower and upper bounds of [Formula: see text] are in line with previous studies. Unusually, [Formula: see text] oscillated in a manner that was "delayed" with the reported deaths. High recent number of deaths were associated (followed) with periods of relatively low disease transmission and low [Formula: see text], and vice-versa. The time-series of Luanda's YF cases suggest the outbreak occurred in two waves, a feature that would have become far more prominent had there been no mass vaccination. The waves could possibly be due to protective reactive behavioral changes of the population affecting the mosquito population. The second wave could well be an outcome of the March-April rainfall patterns in the 2016 El Niño year by creating ideal conditions for the breeding of the mosquito vectors. The modelling framework is a powerful tool for studying future YF epidemic outbreaks, and provides a basis for future vaccination campaign evaluations.

Project description:BackgroundSince late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.MethodsWe jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.FindingsThe early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.InterpretationOur findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy.FundingWellcome Trust.

Project description:Abstract Background Survivors of childhood bacterial meningitis (BM) from low-income countries are at increased risk of sequelae. How BM survivors’ daily life is affected in the developing world, is not known. We aimed to investigate the quality of life among pediatric survivors of BM in Luanda, Angola assessing both physical and psychosocial health-related quality of life (HRQOL). Methods Survivors from two BM treatment trials (ISRCTN62824827; NCT01540838) from Luanda Children’s Hospital were called to follow-up visits in January 2017 with a median duration of 26 months after BM. We administered Pediatric Quality of Life InventoryTM (PedsQLTM) 4.0 Generic Core Scales and Infant Scales, designed to measure HRQOL in children, to patients and/or parents. The generic core scales were administered to 64 and 32 families, and the infant scores to 8 and 3 care-givers of patients and controls, respectively. We used age-appropriate versions of the scales. We calculated the total, physical and psychosocial HRQOL domains. To determine internal consistency, Cronbach’s alpha coefficients were calculated for generic score scales. Results In all, 73 survivors of BM and 36 control children participated. 18 (51%) of controls were siblings of survivors; others visited the hospital for vaccinations. The median age of children was 6 years (81 months, IQR 85 months). Survivors of BM scored significantly lower on the PedsQLTM 4.0 generic core and infant scales than control children according to parent proxy reports indicating lower HRQOL (total score 81.04 vs. 91.30, P = 0.001; physical health 87.50 vs. 100.00, P = 0.0002; psychosocial health 79.06 vs. 90.00, P = 0.001). Child self-report scores were also lower within BM survivors who were able to answer; however, the difference was not statistically significant. In total sample of survivors, the Cronbach’s alphas reached sufficient reliability of 0.70 in all three domains calculated (total score, ?=0.79; physical health ?= 0.77; psychosocial health ?= 0.89 for child self-reports and ?=0.91, ?=0.91, and ?=0.84 for parent proxy-reports, respectively). Conclusion The survivors of pediatric BM endure a clearly suboptimal quality of life compared with siblings and other control children. HRQOL can be measured reliably among BM patients in developing country setting. Disclosures All authors: No reported disclosures.

Dataset Information

Modelling the effective reproduction number of vector-borne diseases: the yellow fever outbreak in Luanda, Angola 2015-2016 as an example.

Publications

Modelling the effective reproduction number of vector-borne diseases: the yellow fever outbreak in Luanda, Angola 2015-2016 as an example.

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