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Evaluating strategies for pandemic response in Delhi using realistic social networks. Huadong Xia Joint work with Kalyani Nagaraj , Jiangzhuo Chen and Madhav Marathe NDSSL, Virginia Tech ICHI 2013. Outline. Background and Contributions Network synthesis and structure analysis
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Evaluating strategies for pandemic response in Delhi using realistic social networks Huadong Xia Joint work with KalyaniNagaraj, Jiangzhuo Chen and MadhavMarathe NDSSL, Virginia Tech ICHI 2013
Outline • Background and Contributions • Network synthesis and structure analysis • Dynamics and intervention policy • Conclusions
Importance of computational epidemiological models • Pandemics cause substantial social, economic and health impacts • 1918 flu pandemic, killed 50-100 million people or 3 to 5 percent of world population. • … • SARS 2003, H1N1 2009, Avian flu (H7N9) 2013 • Mathematical and Computational models have played an important role in understanding and controlling epidemics • controlled experiments are not allowed for ethic consideration. • understand the space-time dynamics of epidemics
Evolution of computational epidemiology models • Realistic network • w/ social structure • Eubank etc. [DIMACS2006] • Meyers etc.[AMS2007] • Barrett etc.[WSC2008] Random Graph Barrat etc.[DPCN2008] Meyers etc.[ORI2010] Compartmental Model Bailey[TMTIDIA1975] Vespignani [PNAS2006]
Networked Epidemiology • Recent years have seen a new approach for understanding and reasoning pertaining to epidemics • Differs from traditional approach that is based on mass action assumptions • Networked Epidemiology: • the main idea is that a better understanding of the characteristics of the social contact network can give better insights into disease dynamics and effective interventions (e.g. vaccination/quarantining strategies), to control an epidemic
What is a network Locations People • Networks capture social interaction pertinent to the disease • We focus on flu like diseases and the appropriate network is a social contact network based on proximity relationship. • Vertex attributes: • age • household size • gender • income • … • Vertex attributes: • (x,y,z) • land use • … • Edge attributes: • activity type: shop, work, school • (start time 1, end time 1) • (start time 2, end time 2) • …
Network Synthesis • How do we get such a network? • In most cases we couldn’t get a precise representation of the network. Given this we need to synthesize the network for a given region. • The type of the network one makes depends on: (i) time available to make such a network (human and computational), (ii) the data available to make the network, (iii) the specific question that one would like to investigate
Contributions • Building on our earlier work, we propose several novel methods to develop a high resolution social contact network. • We use the new methods create a realistic social contact network for National capital of institute. • A detailed study to Delhi population using the generated realistic social contact network: • Detailed analysis to the static structure of the network • A high performance agent based simulation solution to study dynamics and effects of intervention policies . • Comparison study to other cities.
Outline • Background and Contributions • Network synthesis and structure analysis • Dynamics and intervention policy • Conclusions
Synthetic Populations and their contact networks Goal: • Determine whoare whereand when. Process: • Create a statistically accurate baseline population • Assign each individual to a home • Estimate their activities and where these take place • Determine individual’s contacts & locations throughout a day.
Synthetic Population & Contact NetworkGeneric Methodology synthetic population } Data Contact Network people (demographics) census sublocation model location locations contacts between people People-locationGPL gravity activity survey activities contacts with durations
Challenges in network synthesis • Messy data: • Multiple sources • Large scale • Unstructured and Unformatted: Region-specific, no generic solution • Data is limited: especially for developing countries • typically only collective statistics are available • Deduce the realistic disaggregate structure out of aggregate statistics.
Delhi: National Capital Territory of India • Case study: • Delhi (NCT-I): a representative south Asian city that was never studied before. • Statistics: • 13.85 million people in 2001; 22 million in 2011 • Most populous metropolis: 2nd in India; 4th in the world • 573 square miles, 9 regions (refer to the pic) • The Yamuna river going through urban area. • Unique socio-cultural characteristics: • Large slum area • Tropical weather • Environmental hygiene
Data Sources and Generation Methods for Delhi Synthetic Population and Network
Overview • We generated synthetic population and contact network for Delhi. • International population is hard • We develop novel method to create realistic activity templates. • Capture Spatial and demographic variation • This social network provides useful insights toward understanding disease dynamics and intervention efficacies.
People-location network GPL: structural properties • The people-location network GPL: • The degree of a large portion of nonhome Locations have a power law like distribution.
Disease Spread in a Social Network • Within-host disease model: SEIR • Between-host disease model: • probabilistic transmissions along edges of social contact network • from infectious people to susceptible people
Public Health Interventions • Pharmaceutical interventions: vaccination or antiviral changes an individual’s role in the transmission chain • Lower susceptibility or infectiousness • Non-pharmaceutical interventions: social distancing measures change people activities and hence the connectivity of social network • Work closure, school closure, isolation, etc.
epidemic simulation results: Vulnerability • Calibrate R0 to be 1.35 • Vulnerability is defined as: Normalized number of infected over 10,000 runs of random simulations
epidemic simulation results • Calibrate R0 to be 1.35
epidemic simulation results: interventions • four different intervention strategies : • Vaccination is still most effective strategy. • Pharmaceutical interventions is more effective than the non-pharmaceutical. • School closure is more effective than work closure
Two Versions of Delhi Networks • Delhi v1: • Based on very limited data • Generic methodology applicable to any region in world • Delhi v2: • Requires household level micro sample data and other detailed data, not available for all countries • Improvement on results is expected: V2>V1 • to evaluate the network generation model; • to understand importance of different levels of details.
V1 v.s. V2: epidemic Simulations • Impact to Epidemic Dynamics: • V1 exploited activity schedules from US survey, where people travel much more frequently than Indian. Therefore, the two networks show very different epidemic dynamics in base case (without intervention). • Vulnerability distribution of Delhi-V2 is flat comparing to Delhi-V1. Also, Delhi-V2 is less vulnerable than Delhi-V1, due to less frequent travel.
Epidemic Simulations: comparison of three versions • The iterative refinement in V2 and V3 may change our decision in making intervention strategies: • We will have very different prediction to attack rate and the peak value as well as peak date. • In delaying outbreak of disease, school closure is more effective than Antiviral in V1, which is on the contrary in V2 and V3. • V3 is closer to V2 generally, but it is similar to V1 in terms of Attack rate.
Conclusions • Novel methodology in creating a realistic social contact network for a typical urban area in developing countries • Detailed structure analysis reveal: • Generic properties for large scale social contact network • Region specific features are captured in the model • Simulation study shows: • The epidemic dynamics of the region is strongly influenced by activity pattern and demographic structure of local residents • Comparison to a coarser network suggests: • A high resolution social contact network helps us make better public health policy
END Questions?
Epidemic Simulations Setup • Disease model • Flu similar to H1N1 in 2009: assume R0=1.35, 1.40, 1.45, 1.60 (only the results when R0=1.35 are shown, but others are similar) • SEIR model: heterogeneous incubation and infectious durations • 10 random seeds every day • Interventions • Vaccination: implemented at the beginning of epidemic; compliance rate 25% • Antiviral: implemented when 1% population are infectious; covers 50% population; effective for 15 days • School closure: implemented when 1% population are infectious; compliance rate 60%; lasts for 21 days • Work closure: implemented when 1% population are infectious; compliance rate 50%; lasts for 21 days • Total five configurations (including base case). Each configuration is simulated for 300 days and 30 replicates
Sensitivity Test I: generalized switch • In network construction, we assign people to locations based on gravity model. • What if locations are assigned randomly? • We randomly switch two people’s locations, illustrated below: • The location switch can be modeled as so called “generalized switch”: • Such switches in people-location network can be used to understand the sensitivity to location assignment.
Sensitivity Test I (on Delhi-V2): Trivial Difference with Location Switch • The sensitivity test of location switch shows that the mobility pattern may not be a significant factor that influences either the social contact structure or the epidemic outcome of the population. • The same conclusion applies to the scenarios in V2 (figures below).
Sensitivity Test II (on Delhi-V2): Significant Impact by Varying Sublocation Size • V2 contains more types of locations than V1. • w: work sublocation size • s: school sublocation size • c: college sublocation size • sp: shopping center sublocation size • o: other place sublocation size • Nevertheless, the same conclusion as for V1 holds.
Sensitivity Test: conclusion • We have run preliminary tests on the generated synthetic populations and networks of Delhi to examine the robustness of our new model. • The first test involves switching locations of specific types of activities. • The second test involves varying the sublocation sizes in the sublocation models which are used for generating networks from the synthetic populations. • Our tests suggest that, identifying appropriate models for people-people contacts at sublocations is more important than finding appropriate models for activity-location assignments. • As a part of the project, we have extended our network sensitivity methodology to understand the effects of network construction method.
V1: synthetic population generation • Population generation Input: Joint distribution of age and gender of the population in Delhi (from the India census 2001) Algorithm: • Normalize the counts in the joint distribution of age and gender into a joint probability table • Create 13.85 million individuals one by one. For each individual: Randomly select a cell c with the probability of each cell of the city. Create a person with the age and gender corresponding to the cell c. End Output: 13.85 million individuals are created, each individual is associated with disaggregate attributes of gender and age.
V2: household distribution – a snapshot • Households are distributed along real streets/community blocks. • V2 avoids to distribute households on rivers, lakes and green land etc. (V1 distribute them uniformly within each 1(miles)*1(miles) block)
V2: the distribution of people in non-household locations • Gravity Model: same as V1. • No people/locations are distributed over the Yamuna river.
V2: synthetic population creation method • Same methodology as we did for US populations: Input: total # of households Aggregate distribution of demographic properties from Census: hh size, householder’s age Household micro-samples Output: Synthetic population with household structure. Each individual is assigned an age and gender. Algorithm: 1. Estimate joint distribution of household size and householder’s age: 1) construct a joint table of hh size and householder’s age: fill in # of samples for each cell 2) multiply total # of households to distributions to calculate marginal totals for the table 3) run IPF to get a convergent joint table 4) normalize: divide counts in each cell with (total # of samples), it’s probability for each cell. (illustrated in next slide) 2. create the synthetic households and population: 1) randomly select a cell with the probability in joint table 2) select a household sample h from all samples associated with that cell uniformly at random 3) create a synthetic household H, so that H has same members as h, each member in H has same demographic attributes as those in h. 4) repeat step 2.1-2.3, until # of synthetic households is equal to the total # of households from Census.
V2: Generating Activity Sequences based on Thane Survey • Extract travel categories based on the socio-economic and demographic profile of the Thane sample (Adults) and school attendance statistics from UIS (students). Adults: (i) zero trip maker (home all day) (ii) commuter (with work activity) (iii) non commuter (makes at least one trip but no work trips) (iv) college (only for those aged 18-21) Kids: (i) school (attends school) (ii) non school (does not attend school) (iii) zero trip maker (home all day) • Thane contains trip start time distributions and trip time distributions for adult commuters and non commuters. • Choose appropriate trips for each individual relevant to his/her respective commuter category (for example, a non worker A should not be assigned a home-work trip, let’s say A’s trips on the day are: home-shop and shop-home). • Sample alternately from trip start time distributions and trip duration distributions to generate time slots for each selected trip, with a constraint that two symmetric trips take equal duration. (e.g, we sample for A: 2:00pm-2:20, home-shop; 3:40-4:00, shop-home) • Generate a sequence of activities between trips to fill in the 24 hours in the day. (e.g, for A: 0:00am-2:00pm, home, 2:20pm-3:40pm, shop, 4:00-11:59pm, home) • Thane survey statistics provide no particular information on school and college trip times and trip durations. As a result following assumptions were made regarding travel patterns for kids (age 0-17) and college attendees (aged 18-21): • Kids aged 0-5 years are assigned same activities as an adult in the household. • Non school attendees are modeled as non commuters (i.e. non working adults). • Fixed daily schedules assigned to all school and college attendees. e.g., College-goers attend college between 9:00 am and 3:00 pm. Remainder of the time is spent at home.