Eric Daudé

The Spread of Dengue in an Endemic Urban Milieu-The Case of Delhi, India

Background Dengue is a major international public health concern, one of the most important arthropod-borne diseases. More than 3.5 billion people are at risk of dengue infection and there are an estimated 390 million dengue infections annually. This prolific increase has been connected to societal changes such as population growth and increasing urbanization generating intense agglomeration leading to proliferation of synanthropic mosquito species. Quantifying the spatio-temporal epidemiology of dengue in large cities within the context of a Geographic Information System is a first step in the identification of socio-economic risk factors. Methodology/Principal Findings This Project has been approved by the ethical committee of Institut Pasteur. Data has been anonymized and de-identified prior to geolocalisation and analysis. A GIS was developed for Delhi, enabling typological characterization of the urban environment. Dengue cases identified in the Delhi surveillance system from 2008 to 2010 were collated, localised and embedded within this GIS. The spatio-temporal distribution of dengue cases and extent of clustering were analyzed. Increasing distance from the forest in Delhi reduced the risk of occurrence of a dengue case. Proximity to a hospital did not increase risk of a notified dengue case. Overall, there was high heterogeneity in incidence rate within areas with the same socio-economical profiles and substantial inter-annual variability. Dengue affected the poorest areas with high density of humans, but rich areas were also found to be infected, potentially because of their central location with respect to the daily mobility network of Delhi. Dengue cases were highly clustered in space and there was a strong relationship between the time of introduction of the virus and subsequent cluster size. At a larger scale, earlier introduction predicted the total number of cases. Conclusions/Significance DENV epidemiology within Delhi has a forest fire signature. The stochastic nature of this invasion process likely smothers any detectable socio-economic risk factors. However, the significant finding that the size of the dengue case cluster depends on the timing of its emergence emphasizes the need for early case detection and implementation of effective mosquito control. A better understanding of the role of population mobility in contributing to dengue risk could also help focus control on areas at particular risk of dengue virus importation.

Urban climate versus global climate change-what makes the difference for dengue?

The expansion in the geographical distribution of vector‐borne diseases is a much emphasized consequence of climate change, as are the consequences of urbanization for diseases that are already endemic, which may be even more important for public health. In this paper, we focus on dengue, the most widespread urban vector‐borne disease. Largely urban with a tropical/subtropical distribution and vectored by a domesticated mosquito, Aedes aegypti, dengue poses a serious public health threat. Temperature plays a determinant role in dengue epidemic potential, affecting crucial parts of the mosquito and viral life cycles. The urban predilection of the mosquito species will further exacerbate the impact of global temperature change because of the urban heat island effect. Even within a city, temperatures can vary by 10 °C according to urban land use, and diurnal temperature range (DTR) can be even greater. DTR has been shown to contribute significantly to dengue epidemic potential. Unraveling the importance of within‐city temperature is as important for dengue as for the negative health consequences of high temperatures that have thus far been emphasized, for example, pollution and heat stroke. Urban and landscape planning designed to mitigate the non‐infectious negative effects of temperature should additionally focus on dengue, which is currently spreading worldwide with no signs of respite.

Widespread Fear of Dengue Transmission but Poor Practices of Dengue Prevention: A Study in the Slums of Delhi, India

Background This study has been conducted to throw light on the knowledge and practices related to dengue fever among the poor population living in Delhi’s slums. Materials A household survey was conducted in 2013 among 3,350 households. The households were stratified by a number of variables related to socio-economic status and health events such as hospitalisation. The data collection was completed through face-to-face interviews conducted with the help of 25 field investigators. Results About 8% of the households had at least one diagnosed dengue case. In comparison to the population surveyed, teenagers (15–19 years) and adults (30–34 years) were more affected whereas children under four years of age were underrepresented. Housewives are more affected by dengue (24%) compared to their share of the population surveyed (17%). Despite the fact that 77% of the respondents are worried about mosquitoes, only 43% of them monitor environment to avoid the presence of breeding sites. Conclusion One cannot exclude the possibility that though young children under the age of four years are exposed to the virus, either their cases were asymptomatic or family members infected during this period had potentially more serious symptoms leading to hospitalisation. This result could thus be explained by budget-related health choices made by this population which do not favour small children. Educational programs should target housewives to improve their impact, as they are the ones mostly responsible for water storage and cleanliness of the house and its neighbourhood. Even with a dengue experience and potentially an acute perception of the risk and its factors, a proper management of environmental conditions is lacking. This along with the fact that word-of-mouth is the main source of information quoted should be a message for municipality health workers to give door-to-door information on how to prevent breeding sites and dengue infection.

MOSAIIC: City-Level Agent-Based Traffic Simulation Adapted to Emergency Situations

In this paper, we present MOSAIIC, an agent-based model to simulate the road traffic of a city in the context of a catastrophic event. Whether natural (cyclone, earthquake, flood) or human (industrial accident) in origin, catastrophic situations modify both infrastructures (buildings, road networks) and human behaviors, which can have a huge impact on human safety. Because the heterogeneities of human behaviors, of land-uses and of network topology have a great impact on the traffic flows, the agent-based modeling is particularly adapted to this subject. In this paper, we focus on the new traffic model itself: the way geographical data is used to build a network, the various behaviors of our agents, from the individual to the collective level.

Introduction to NetLogo

Abstract NetLogo is a programming environment which allows for the construction and exploration of agent-based models. Developed at the Center for Connected Learning, the software currently draws from StarLogoT, which is available for Mac OSX, and StarLogo, which was developed at MIT’s Media Laboratory. It is the latter that has had the greatest influence on the programming language used by NetLogo, known as Logo, which was itself inspired by the Lisp programming language family. The history of Logo allows for a partial understanding of NetLogo’s philosophy.

A Multi-modal Urban Traffic Agent-Based Framework to Study Individual Response to Catastrophic Events

Urban traffic is made of a variety of mobility modes that have to be taken into account to explore the impact of catastrophic event. From individual mobility behaviors to macroscopic traffic dynamics, agent-based modeling provides an interesting conceptual framework to study this question. Unfortunately, most proposals in the domain do not provide any simple way to model these multi-modal trajectories, and thus fell short at simulating in a credible way the outcomes of a catastrophic event, like natural or industrial hazards. This paper presents an agent-based framework implemented with the GAMA modeling platform that aims at overcoming this lack. An application of this model for the study of flood crisis in a district of Hanoi (Vietnam) is presented.

3 – Macro Models, Micro Models and Network-based Coupling

Abstract: In this chapter, we will discuss coupling models with different scales to describe the propagation of a virus within a population. This population is distributed throughout a set of cities connected by airline routes. Population movements between cities enable the virus to travel, carried by infected individuals. In each city, the description of virus propagation is based on an SIR-type model (presented in more detail). The first model, called EpiSim, is presented, first in its aggregate variant, and then in its individual-based variant. Comparing these two approaches will allow us to discuss the advantages and limitations of each of them. In the second section, we will present two approaches for coupling models based on networks. The first approach considers a network of coupled systems of equations. The second combines an agent-based model of microscale components with a model of macroscale components based on systems of equations. This coupled model, called MicMac, is presented in more detail. The added value of micro/macro coupling is examined in the article.