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

2021 ◽  
Vol 15 (2) ◽  
pp. e0009106
Author(s):  
Ayesha S. Mahmud ◽  
Md. Iqbal Kabir ◽  
Kenth Engø-Monsen ◽  
Sania Tahmina ◽  
Baizid Khoorshid Riaz ◽  
...  
Keyword(s):  
Large Scale ◽  
Mechanistic Model ◽  
Household Survey ◽  
Epidemiological Data ◽  
Mobile Phone Data ◽  
World Population ◽  
Population Mobility ◽  
Time Data ◽  
Middle Income ◽  
The Impact

Background Several large outbreaks of chikungunya have been reported in the Indian Ocean region in the last decade. In 2017, an outbreak occurred in Dhaka, Bangladesh, one of the largest and densest megacities in the world. Population mobility and fluctuations in population density are important drivers of epidemics. Measuring population mobility during outbreaks is challenging but is a particularly important goal in the context of rapidly growing and highly connected cities in low- and middle-income countries, which can act to amplify and spread local epidemics nationally and internationally. Methods We first describe the epidemiology of the 2017 chikungunya outbreak in Dhaka and estimate incidence using a mechanistic model of chikungunya transmission parametrized with epidemiological data from a household survey. We combine the modeled dynamics of chikungunya in Dhaka, with mobility estimates derived from mobile phone data for over 4 million subscribers, to understand the role of population mobility on the spatial spread of chikungunya within and outside Dhaka during the 2017 outbreak. Results We estimate a much higher incidence of chikungunya in Dhaka than suggested by official case counts. Vector abundance, local demographics, and population mobility were associated with spatial heterogeneities in incidence in Dhaka. The peak of the outbreak in Dhaka coincided with the annual Eid holidays, during which large numbers of people traveled from Dhaka to other parts of the country. We show that travel during Eid likely resulted in the spread of the infection to the rest of the country. Conclusions Our results highlight the impact of large-scale population movements, for example during holidays, on the spread of infectious diseases. These dynamics are difficult to capture using traditional approaches, and we compare our results to a standard diffusion model, to highlight the value of real-time data from mobile phones for outbreak analysis, forecasting, and surveillance.

scholarly journals Megacities as drivers of national outbreaks: the role of holiday travel in the spread of infectious diseases

2019 ◽  
Author(s):  
Ayesha S. Mahmud ◽  
Md. Iqbal Kabir ◽  
Kenth Engø-Monsen ◽  
Sania Tahmina ◽  
Baizid Khoorshid Riaz ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Large Scale ◽  
Disease Risk ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Dhaka City ◽  
Population Mobility ◽  
Time Data ◽  
The Impact

AbstractHuman mobility connects populations and can lead to large fluctuations in population density, both of which are important drivers of epidemics. Measuring population mobility during infectious disease outbreaks is challenging, but is a particularly important goal in the context of rapidly growing and highly connected urban centers in low and middle income countries, which can act to amplify and spread local epidemics nationally and internationally. Here, we combine estimates of population movement from mobile phone data for over 4 million subscribers in the megacity of Dhaka, Bangladesh, one of the most densely populated cities globally. We combine mobility data with epidemiological data from a household survey, to understand the role of population mobility on the spatial spread of the mosquito-borne virus chikungunya within and outside Dhaka city during a large outbreak in 2017. The peak of the 2017 chikungunya outbreak in Dhaka coincided with the annual Eid holidays, during which large numbers of people traveled from Dhaka to their native region in other parts of the country. We show that regular population fluxes around Dhaka city played a significant role in determining disease risk, and that travel during Eid was crucial to the spread of the infection to the rest of the country. Our results highlight the impact of large-scale population movements, for example during holidays, on the spread of infectious diseases. These dynamics are difficult to capture using traditional approaches, and we compare our results to a standard diffusion model, to highlight the value of real-time data from mobile phones for outbreak analysis, forecasting, and surveillance.

scholarly journals Novel transdisciplinary methodology for cross-sectional analysis of snakebite epidemiology at national scale

2021 ◽  
Vol 15 (2) ◽  
pp. e0009023
Author(s):  
Gabriel Alcoba ◽  
Carlos Ochoa ◽  
Sara Babo Martins ◽  
Rafael Ruiz de Castañeda ◽  
Isabelle Bolon ◽  
...  
Keyword(s):  
Large Scale ◽  
Survey Methodology ◽  
Neglected Tropical Diseases ◽  
Animal Health ◽  
Epidemiological Data ◽  
World Health ◽  
Primary Data ◽  
Cross Sectional ◽  
High Resolution Satellite Images ◽  
The Impact

Background Worldwide, it is estimated that snakes bite 4.5–5.4 million people annually, 2.7 million of which are envenomed, and 81,000–138,000 die. The World Health Organization reported these estimates and recognized the scarcity of large-scale, community-based, epidemiological data. In this context, we developed the “Snake-Byte” project that aims at (i) quantifying and mapping the impact of snakebite on human and animal health, and on livelihoods, (ii) developing predictive models for medical, ecological and economic indicators, and (iii) analyzing geographic accessibility to healthcare. This paper exclusively describes the methodology we developed to collect large-scale primary data on snakebite in humans and animals in two hyper-endemic countries, Cameroon and Nepal. Methodology/Principal findings We compared available methods on snakebite epidemiology and on multi-cluster survey development. Then, in line with those findings, we developed an original study methodology based on a multi-cluster random survey, enhanced by geospatial, One Health, and health economics components. Using a minimum hypothesized snakebite national incidence of 100/100,000/year and optimizing design effect, confidence level, and non-response margin, we calculated a sample of 61,000 people per country. This represented 11,700 households in Cameroon and 13,800 in Nepal. The random selection with probability proportional to size generated 250 clusters from all Cameroonian regions and all Nepalese Terai districts. Our household selection methodology combined spatial randomization and selection via high-resolution satellite images. After ethical approval in Switerland (CCER), Nepal (BPKIHS), and Cameroon (CNERSH), and informed written consent, our e-questionnaires included geolocated baseline demographic and socio-economic characteristics, snakebite clinical features and outcomes, healthcare expenditure, animal ownership, animal outcomes, snake identification, and service accessibility. Conclusions/Significance This novel transdisciplinary survey methodology was subsequently used to collect countrywide snakebite envenoming data in Nepal and Cameroon. District-level incidence data should help health authorities to channel antivenom and healthcare allocation. This methodology, or parts thereof, could be easily adapted to other countries and to other Neglected Tropical Diseases.

scholarly journals A viral perspective on worldwide non-pharmaceutical interventions against COVID-19

2020 ◽  
Author(s):  
Jean-Philippe Rasigade ◽  
Anaïs Barray ◽  
Julie Teresa Shapiro ◽  
Charlène Coquisart ◽  
Yoann Vigouroux ◽  
...  
Keyword(s):  
Large Scale ◽  
Reproduction Number ◽  
Cumulative Effect ◽  
Epidemiological Data ◽  
Time And Space ◽  
Minimal Set ◽  
Genomic Divergence ◽  
The Impact ◽  
Pharmaceutical Interventions

AbstractQuantifying the effectiveness of large-scale non-pharmaceutical interventions (NPIs) against COVID-19 is critical to adapting responses against future waves of the pandemic. Most studies of NPIs thus far have relied on epidemiological data. Here, we report the impact of NPIs on the evolution of SARS-CoV-2, taking the perspective of the virus. We examined how variations through time and space of SARS-CoV-2 genomic divergence rates, which reflect variations of the epidemic reproduction number Rt, can be explained by NPIs and combinations thereof. Based on the analysis of 5,198 SARS-CoV-2 genomes from 57 countries along with a detailed chronology of 9 non-pharmaceutical interventions during the early epidemic phase up to May 2020, we find that home containment (35% Rt reduction) and education lockdown (26%) had the strongest predicted effectiveness. To estimate the cumulative effect of NPIs, we modelled the probability of reducing Rt below 1, which is required to stop the epidemic, for various intervention combinations and initial Rt values. In these models, no intervention implemented alone was sufficient to stop the epidemic for Rt’s above 2 and all interventions combined were required for Rt’s above 3. Our approach can help inform decisions on the minimal set of NPIs required to control the epidemic depending on the current Rt value.

Safe Reopening Following Generalized Lockdowns: A Strategy Based on Evidence from 24 Worldwide Countries

2021 ◽  
Author(s):  
Abu S. Shonchoy ◽  
Khandker S. Ishtiaq ◽  
Sajedul Talukder ◽  
Nasar U. Ahmed ◽  
Rajiv Chowdhury
Keyword(s):  
Low Income ◽  
Large Scale ◽  
Simulation Analysis ◽  
Epidemiological Data ◽  
World Health ◽  
Optimal Timing ◽  
Middle Income ◽  
Capacity Limits ◽  
Positive Rate ◽  
Health Organization

Abstract While the effectiveness of lockdowns to reduce Coronavirus Disease-2019 (COVID-19) transmission is well established, uncertainties remain on the lifting principles of these restrictive interventions. World Health Organization recommends case positive rate of 5% or lower as a threshold for safe reopening. However, inadequate testing capacity limits the applicability of this recommendation, especially in the low-income and middle-income countries (LMICs). To develop a practical reopening strategy for LMICs, in this study, we first identify the optimal timing of safe reopening by exploring accessible epidemiological data of 24 countries during the initial COVID-19 surge. We find that safely reopening requires a two-week waiting period, after the crossover of daily infection and recovery rates – coupled with a post-crossover continuous negative trend in daily new cases. Epidemiologic SIRM model-based simulation analysis validates our findings. Finally, we develop an easily interpretable large-scale reopening (LSR) index, which is an evidence-based toolkit – to guide/inform the reopening decisions for LMICs.

More than poverty

2016 ◽  
Vol 40 (6) ◽  
pp. 536-543 ◽  
Author(s):  
Theodore D. Wachs ◽  
Santiago Cueto ◽  
Haogen Yao
Keyword(s):  
Low Income ◽  
Large Scale ◽  
Cumulative Risk ◽  
Low Income Countries ◽  
Data Set ◽  
Middle Income ◽  
Middle Income Countries ◽  
Developmental Risks ◽  
The Impact ◽  
Low And Middle Income

Studies from both high and low-middle income (LAMI) countries have documented how being reared in poverty is linked to compromised child development. Links between poverty and development are mediated by the timing and extent of exposure to both risk factors nested under poverty and to protective influences which can attenuate the impact of risk. While children from high-, middle-, and low-income countries are exposed to similar types of developmental risks, children from low- and middle-income countries are exposed to a greater number, more varied and more intense risks. Given these contextual differences, cumulative risk models may provide a better fit than mediated models for understanding the nature of pathways linking economic insufficiency and developmental inequality in low- and middle-income countries, and for designing interventions to promote development of children from these countries. New evidence from a large scale UNICEF data set illustrates the application of a cumulative risk/protective perspective in low- and middle-income countries.

scholarly journals Systems-based approach for optimization of a scalable bacterial ST mapping assembly-free algorithm

2021 ◽  
Author(s):  
Natasha Pavlovikj ◽  
Joao Carlos Gomes-Neto ◽  
Jitender S. Deogun ◽  
Andrew K. Benson
Keyword(s):  
Large Scale ◽  
Population Genomics ◽  
Bacterial Pathogens ◽  
Population Based ◽  
Species Level ◽  
Epidemiological Surveillance ◽  
Time Data ◽  
Bacterial Populations ◽  
Genomic Epidemiology ◽  
The Impact

Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turn-around, while aiming at generating two main outcomes: 1) Species level identification; and 2) Variant mapping at different levels of genotypic resolution for population-based tracking, in addition to predicting traits such as antimicrobial resistance (AMR). With the recent advances and continual dissemination of whole-genome sequencing technologies, large-scale population-based genotyping of bacterial pathogens has become possible. Since bacterial populations often present a high degree of clonality in the genomic backbone (i.e., low genetic diversity), the choice of genotyping scheme can even facilitate the understanding of ancestral relationships and can be used for prediction of co-inherited traits such as AMR. Multi-locus sequence typing (MLST) fits that purpose and can identify sequence types (ST) based on seven ubiquitous genome-scattered loci that aid in genotyping isolates beneath the species level. ST-based mapping also standardizes genotyping across laboratories and can be consistently used worldwide. However, ST-based algorithms, when using Illumina paired-end sequences, often rely on genome assembly prior to classification. That hinders rapid genotyping and scalability which are essential aspects of genomic epidemiology. stringMLST is a kmer-based ST method with the capacity to solve both hurdles. Yet, a comprehensive scalable comparison of its use in contrast to a standard MLST program for a wide array of phylogenetically divergent Public Health-relevant bacterial pathogens is lacking. Herein, we first demonstrated that stringMLST is a fast tool that can be deployed for ST-based epidemiological inquiries of bacterial populations. Additionally, we systematically evaluated and showed the impact of genome-intrinsic and -extrinsic features, as well as the optimal kmer length in maximizing the performance of stringMLST on species-by-species basis, and highlighted a few instances where this program may not be applicable in its current format. Furthermore, we integrated stringMLST as part of our freely available and scalable hierarchical-based population genomics platform called ProkEvo. Besides facilitating automatable and reproducible bacterial population guided analysis, ProkEvo now offers a rapidly deployable genomic epidemiology tool for ST mapping, with specific guidance on how to optimize its performance, that can be widely applicable by microbiological laboratories and epidemiological agencies.

scholarly journals Vaccinations, Mobility and COVID-19 Transmission

2021 ◽  
Vol 19 (1) ◽  
pp. 97
Author(s):  
Jianfeng Guo ◽  
Chao Deng ◽  
Fu Gu
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Large Scale ◽  
Equation Modeling ◽  
World Population ◽  
Human Behaviors ◽  
The World ◽  
Positive Correlations ◽  
The Impact ◽  
Pharmaceutical Interventions

In order to prevent the spread of coronavirus disease 2019 (COVID-19), 52.4% of the world population had received at least one dose of a vaccine at17 November 2021, but little is known about the non-pharmaceutical aspect of vaccination. Here we empirically examine the impact of vaccination on human behaviors and COVID-19 transmission via structural equation modeling. The results suggest that, from a non-pharmaceutical perspective, the effectiveness of COVID-19 vaccines is related to human behaviors, in this case, mobility; vaccination slows the spread of COVID-19 in the regions where vaccination is negatively related to mobility, but such an effect is not observed in the regions where vaccination and mobility have positive correlations. This article highlights the significance of mobility in realizing the effectiveness of COVID-19 vaccines; even with large-scale vaccination, non-pharmaceutical interventions, such as social distancing, are still required to contain the transmission of COVID-19.

scholarly journals How do farmland rental markets affect farmers’ income? Evidence from a matched renting-in and renting-out household survey in Northeast China

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256590
Author(s):  
Ning Geng ◽  
Zhifeng Gao ◽  
Chuchu Sun ◽  
Mengyao Wang
Keyword(s):  
Land Reform ◽  
Large Scale ◽  
Household Survey ◽  
Scale Economy ◽  
Farm Income ◽  
Agricultural Cooperatives ◽  
System Reform ◽  
Rental Market ◽  
Rental Markets ◽  
The Impact

Promoting farmland transfer through the farmland rental market is an essential instrument to achieve the scale economy of agricultural production in China. However, past literature on the land reform in China pays more attention to the renting-in household or the renting-out household, respectively, less to both types of households together. Using a large-scale survey of farm households in China, we examine the determinants of participation in the farmland rental market and quantify the impact of the rental market on farmers’ income. Findings show household off-farm income, family members’ part-time employment, agricultural subsidies, and participation in agricultural cooperatives significantly affect farmers’ participation in the farmland rental market. Participation in the farmland rental market significantly increases the income of renting-in households, while it decreases the income of renting-out households, which might result from the temporary lag effect of the land system reform.

scholarly journals Evaluation of Work Resumption Strategies after COVID-19 Reopening in the Chinese City of Shenzhen: A Mathematical Modeling Study

2020 ◽  
Author(s):  
Lu Bai ◽  
Haonan Lu ◽  
Hailin Hu ◽  
M. Kumi Smith ◽  
Katherine Harripersaud ◽  
...  
Keyword(s):  
Population Control ◽  
Epidemiological Data ◽  
Population Mobility ◽  
Mobility Data ◽  
Intervention Measures ◽  
Imported Cases ◽  
High Level ◽  
The Impact ◽  
Second Wave ◽  
Work Resumption

Abstract BackgroundAs China is facing a potential second wave of the epidemic, we reviewed and evaluated the intervention measures implemented in a major metropolitan city, Shenzhen, during the early phase of Wuhan lockdown. MethodsBased on published epidemiological data on COVID-19 and population mobility data from Baidu Qianxi, we constructed a compartmental model to evaluate the impact of work and traffic resumption on the epidemic in Shenzhen in various scenarios.ResultsImported cases account for the majority (58.6%) of the early reported cases in Shenzhen. We demonstrated that with strict inflow population control and a high level of mask usage following work resumption, various resumption schemes resulted in only an insignificant difference in the number of cumulative infections. Shenzhen may experience this second wave of infections approximately two weeks after the traffic resumption if the incidence risk in Hubei is high at the moment of resumption.ConclusionControl of imported cases and extensive use of facial masks were the key for the prevention of the COVID-19 epidemic in Shenzhen during its reopening and work resumption.

scholarly journals The Attention Economy and the Impact of Artificial Intelligence

2021 ◽  
pp. 123-134
Author(s):  
Ricardo Baeza-Yates ◽  
Usama M. Fayyad
Keyword(s):  
Large Scale ◽  
Cognitive Biases ◽  
Information Overload ◽  
Online Advertising ◽  
World Population ◽  
New Science ◽  
Ethical Implications ◽  
Attention Economy ◽  
Digital Manipulation ◽  
The Impact

AbstractThe growing ubiquity of the Internet and the information overload created a new economy at the end of the twentieth century: the economy of attention. While difficult to size, we know that it exceeds proxies such as the global online advertising market which is now over $300 billion with a reach of 60% of the world population. A discussion of the attention economy naturally leads to the data economy and collecting data from large-scale interactions with consumers. We discuss the impact of AI in this setting, particularly of biased data, unfair algorithms, and a user-machine feedback loop tainted by digital manipulation and the cognitive biases of users. The impact includes loss of privacy, unfair digital markets, and many ethical implications that affect society as a whole. The goal is to outline that a new science for understanding, valuing, and responsibly navigating and benefiting from attention and data is much needed.

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