Megacities as drivers of national outbreaks: The 2017 chikungunya outbreak in Dhaka, Bangladesh

Alarming Trends in Dengue Incidence and Mortality in Bangladesh

Bangladesh is currently experiencing the country's largest and deadliest dengue outbreak on record. This year's outbreak has been characterized by an early seasonal surge in cases, rapid geographic spread, and a high fatality rate. The alarming trends in dengue incidence and mortality this year is an urgent wake-up call for public health policymakers and researchers to pay closer attention to dengue dynamics in South Asia, to strengthen the surveillance system and diagnostic capabilities, and to develop tools and methods for guiding strategic resource allocation and control efforts.

The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting

Although the drivers of influenza have been well studied in high-income settings in temperate regions, many open questions remain about the burden, seasonality, and drivers of influenza dynamics in the tropics. In temperate climates, the inverse relationship between specific humidity and transmission can explain much of the observed temporal and spatial patterns of influenza outbreaks. Yet, this relationship fails to explain seasonality, or lack there-of, in tropical and subtropical countries. Here, we analyzed eight years of influenza surveillance data from 12 locations in Bangladesh to quantify the role of climate in driving disease dynamics in a tropical setting with a distinct rainy season. We find strong evidence for a nonlinear bimodal relationship between specific humidity and influenza transmission in Bangladesh, with highest transmission occurring for relatively low and high specific humidity values. We simulated influenza burden under current and future climate in Bangladesh using a mathematical model with a bimodal relationship between humidity and transmission, and decreased transmission at very high temperatures, while accounting for changes in population immunity. The climate-driven mechanistic model can accurately capture both the temporal and spatial variation in influenza activity observed across Bangladesh, highlighting the usefulness of mechanistic models for low-income countries with inadequate surveillance. By using climate model projections, we also highlight the potential impact of climate change on influenza dynamics in the tropics and the public health consequences.

Insecticide resistance compromises the control of Aedes aegypti in Bangladesh

BACKGROUND

With no effective drugs or widely available vaccines, dengue control in Bangladesh is dependent on targeting the primary vector Aedes aegypti with insecticides and larval source management. Despite these interventions, the dengue burden is increasing in Bangladesh, and the country experienced its worst outbreak in 2019 with 101 354 hospitalized cases. This may be partially facilitated by the presence of intense insecticide resistance in vector populations. Here, we describe the intensity and mechanisms of resistance to insecticides commonly deployed against Ae. aegypti in Dhaka, Bangladesh.

RESULTS

Dhaka Ae. aegypti colonies exhibited high-intensity resistance to pyrethroids. Using CDC bottle assays, we recorded 2-24% mortality (recorded at 24 h) to permethrin and 48-94% mortality to deltamethrin, at 10× the diagnostic dose. Bioassays conducted using insecticide-synergist combinations suggested that metabolic mechanisms were contributing to pyrethroid resistance, specifically multi-function oxidases, esterases, and glutathione S-transferases. In addition, kdr alleles were detected, with a high frequency (78-98%) of homozygotes for the V1016G mutation. A large proportion (≤ 74%) of free-flying and resting mosquitoes from Dhaka colonies survived exposure to standard applications of pyrethroid aerosols in an experimental free-flight room. Although that exposure affected the immediate host-seeking behavior of Ae. aegypti, the effect was transient in surviving mosquitoes.

CONCLUSION

The intense resistance characterized in this study is likely compromising the operational effectiveness of pyrethroids against Ae. aegypti in Dhaka. Switching to alternative chemical classes may offer a medium-term solution, but ultimately a more sustainable and effective approach to controlling dengue vectors is required. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Socioeconomic differences in COVID-19 infection, hospitalisation and mortality in urban areas in a region in the South of Europe

BACKGROUND

To analyse differences in confirmed cases, hospitalisations and deaths due to COVID-19 related to census section socioeconomic variables.  METHODS: Ecological study in the 12 largest municipalities in Andalusia (Spain) during the first three epidemic waves of the COVID-19 (02/26/20-03/31/21), covering 2,246 census sections (unit of analysis) and 3,027,000 inhabitants. Incidence was calculated, standardised by age and sex, for infection, hospitalisation and deaths based on average gross income per household (AGI) for the census tracts in each urban area. Association studied using a Poisson Bayesian regression model with random effects for spatial smoothing.

RESULTS

There were 140,743 cases of COVID-19, of which 12,585 were hospitalised and 2,255 died. 95.2% of cases were attributed to the second and third waves, which were jointly analysed. We observed a protective effect of income for infection in 3/12 cities. Almeria had the largest protective effect (smoothed relative risk (SRR) = 0.84 (0.75-0.94 CI 95%). This relationship reappeared with greater magnitude in 10/12 cities for hospitalisation, lowest risk in Algeciras SRR = 0.41 (0.29-0.56). The pattern was repeated for deaths in all urban areas and reached statistical significance in 8 cities. Lowest risk in Dos Hermanas SRR = 0.35 (0.15-0.81).

CONCLUSIONS

Income inequalities by geographical area were found in the incidence of COVID-19. The strengths of the association increased when analysing the severe outcomes of hospitalisations and, above all, deaths.

Connecting the dots: understanding how human mobility shapes TB epidemics

Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source-sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology.

Megacity-centric mass mobility during Eid holidays: a unique concern for infectious disease transmission in Bangladesh

Human mobility, particularly during certain festivals in rapidly growing megacities in low- and middle-income countries, has critical implications in infectious diseases surveillance and preparedness. In this perspective, we present the interesting case of Dhaka megacity, the capital of Bangladesh with a population of over 20 million. In recent times, three massive infectious disease outbreaks in Dhaka (chikungunya, dengue and COVID-19) coincided with Muslim religious Eid festivals. From a public health standpoint, it is very important to share this information with the international community to fight against emerging infectious diseases around the world.

Chikungunya outbreak in Bangladesh (2017): sociodemographic and clinical characteristics of patients from three hotspots

BACKGROUND

Chikungunya is a severely debilitating disease. Bangladesh witnessed one of the largest outbreaks in 2017. Here, we described the clinical profile of the chikungunya outbreak in Bangladesh and its heterogeneity across three hotspots.

METHODS

This was a descriptive cross-sectional study of 432 individuals interviewed from the outpatient department of three study sites (Dhaka, Chittagong, and Sitakundu Upazilla of Bangladesh) after confirmation by the study physicians. Both laboratory-confirmed cases and probable cases were recruited between July and October 2017.

RESULTS

Of all, 18% (79) were laboratory confirmed, and 353 82% (335) were probable cases. The male:female ratio was almost equal (1.09:1), and the predominant age group was 18-59 years. The mean age of the presentation was 36.07 ± 13.62 (SD) years. Fever and arthralgia were the most common presentations and were present in > 95% of cases. Other frequent symptoms were fatigue, myalgia, headache, nausea, and vomiting. Approximately half of the patients had arthritis and erythematous rash. Arthritis was predominant in Chittagong city, while maculopapular rash was not observed in Sitakunda city. However, fatigue, nausea, and vomiting are more common among patients in Dhaka city. Significant heterogeneity of clinical manifestations was present across the three hotspots (p < 0.05 for all). Both confirmed and probable cases shared similar characteristics except muscle ache (p = 0.22) and rash (p = 0.37).

CONCLUSION

The clinical profile of chikungunya virus-induced disease displays significant location-related heterogeneity in Bangladesh during a large outbreak. Although the causes of such differences are unclear, improved public and medical personnel education on this condition may lead to earlier diagnosis and treatment.

Infectious disease in an era of global change

The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013-2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.

Distinguishing gene flow between malaria parasite populations

Measuring gene flow between malaria parasite populations in different geographic locations can provide strategic information for malaria control interventions. Multiple important questions pertaining to the design of such studies remain unanswered, limiting efforts to operationalize genomic surveillance tools for routine public health use. This report examines the use of population-level summaries of genetic divergence (F_ST) and relatedness (identity-by-descent) to distinguish levels of gene flow between malaria populations, focused on field-relevant questions about data size, sampling, and interpretability of observations from genomic surveillance studies. To do this, we use P. falciparum whole genome sequence data and simulated sequence data approximating malaria populations evolving under different current and historical epidemiological conditions. We employ mobile-phone associated mobility data to estimate parasite migration rates over different spatial scales and use this to inform our analysis. This analysis underscores the complementary nature of divergence- and relatedness-based metrics for distinguishing gene flow over different temporal and spatial scales and characterizes the data requirements for using these metrics in different contexts. Our results have implications for the design and implementation of malaria genomic surveillance studies.

Malaria is a leading infectious cause of illness and death worldwide. Understanding how malaria parasites are spread between different geographic locations can provide useful information for disease control efforts. Examples include identifying source locations for imported infections in lower-incidence “sink” locations and delineating the routes over which drug-resistant malaria strains disperse across geographic space. Genomic surveillance methods use geolocated genetic sequence data from malaria infections to estimate gene flow and connectivity between parasites populations in different locations. This approach has yielded important insights into patterns of connectivity between malaria populations over local, national, and global scales. However, there are multiple unresolved questions about the design and interpretation of these studies. This study evaluates how much data is needed to distinguish different levels of gene flow between parasite populations (“Are the malaria populations in locations i and j linked by higher or lower connectivity than those in locations k and l?”). We examine data size requirements (including the number of genetic markers and number of individual infections analyzed) for this important, implementation-relevant task across multiple epidemiological scenarios, providing practical guidance for the design and interpretation of similar studies.

Challenges in evaluating risks and policy options around endemic establishment or elimination of novel pathogens

When a novel pathogen emerges there may be opportunities to eliminate transmission - locally or globally - whilst case numbers are low. However, the effort required to push a disease to elimination may come at a vast cost at a time when uncertainty is high. Models currently inform policy discussions on this question, but there are a number of open challenges, particularly given unknown aspects of the pathogen biology, the effectiveness and feasibility of interventions, and the intersecting political, economic, sociological and behavioural complexities for a novel pathogen. In this overview, we detail how models might identify directions for better leveraging or expanding the scope of data available on the pathogen trajectory, for bounding the theoretical context of emergence relative to prospects for elimination, and for framing the larger economic, behavioural and social context that will influence policy decisions and the pathogen’s outcome.

Thinking clearly about social aspects of infectious disease transmission

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2

In response to the SARS-CoV-2 pandemic, unprecedented travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns—defined as restrictions on both local movement or long distance travel—will determine how effective these kinds of interventions are. Here, we evaluate the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. We show that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. Our model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread. Lockdowns play a key role in reducing contacts and controlling outbreaks, but appropriate messaging surrounding their announcement and careful evaluation of changes in mobility are needed to mitigate the possible unintended consequences.