Maintaining Elimination in an Environment of Persistent Importation

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Presentation transcript:

Maintaining Elimination in an Environment of Persistent Importation Kurt Frey April 15, 2019

Measles surveillance within an agent-based modeling framework IDM measles aims to model the major components of an eradication strategy: Routine Immunization Scheduled catch-up and maintenance campaigns Disease surveillance Reactive campaigns Today’s focus is on surveillance and response for agent-based simulations: Incidence Focus Susceptibility Focus Surveillance Contexts Model Calibration Reporting, Extent, Promptness Efficiency Known / Inferred

Total susceptibility is the main factor in measles burden calculations Timeseries data informs transmission dynamics, not total burden. Endemic and near-elimination contexts assume nearly universal exposure to infection. Multi-year susceptible accumulation only occurs after interrupting transmission. WHO Reported Cases 2015-2017 Country Cases MCV1 India 211 216 82 China 74 448 --- Mongolia 49 095 86 Nigeria 41 160 42 Indonesia 24 802 80 Ethiopia 24 258 55 Kyrgyzstan 17 784 96 D. R. Congo 13 578 72 Pakistan 10 703 61 Mongolia Nigeria 16k 12k Nigeria and Mongolia are examples of the contrast between endemic and re-introduction contexts. 8k Reported Cases 4k 2011 2012 2013 2014 2015 2016 2017 2018

Current agent-based simulations of measles transmission in Nigeria Parameters appropriate to Nigeria were determined from reported age-at-infection and timeseries case reports. High Infectivity Season 160 120 Populations at the admin-2 level were well-mixed and networked using a gravity-type model. Supplementary immunizations (SIAs) followed the calendar of recorded activities for the period 2005 - 2015. Duration (Days) 80 40 Sept Oct Nov Dec Jan Start Date 2k 0.90 60/k 1k 0.45 40/k 0.00 20/k Population Density (km-2) Geographic Centers Routine Vaccination - Mean Fraction Crude Birth Rates (yr-1)

Simulations parameters selected for a focus on near-elimination Calibrated infectivity parameters were combined with a hypothetical schedule of SIAs. SIA coverage was adjusted so interrupting transmission was likely, but maintaining elimination was uncommon. 1/ year 1/ year 100 1/ 2 years 1/ 3 years 75 Months with Incidence (%) 50 Targeted mean SIA coverages interrupted transmission in >90% of simulations. Median monthly burden was non-zero due to re-introduction. Continuous Transmission 25 Increasing SIA Coverage

National Monthly Incidence Total burden was stable; timeseries outcomes were highly variable Mean cumulative burden was around 5M total cases and ranged narrowly. Distribution in time varied substantially. 104 Frequency 102 1.0M Selected Examples 100 0.8M 1M 2M 3M 4M Annual Incidence 0.6M National Monthly Incidence Both small outbreaks (<1k cases) and multi-year intervals between large outbreaks were common. 0.4M 0.2M 0.0M 5 10 15 20 Simulation Year

Additional SIAs were introduced in response to observed incidence Recently added capability for measles EMOD simulations allows interventions as response to symptomatic incidence. Burden Reduction (%) 70% 90% 250 Two consecutive two-week periods with observed incidence signaled an outbreak. Response was implemented rapidly (<3 weeks) after an outbreak. Above 250km radius is the scale of a regional campaign. 100 50% 200 150 50 Response Radius (km) 30% 100 50 10% 0.1 1.0 10 100 Surveillance Rate (%)

Delays after outbreak identification limits maximum impact Increased delay between outbreak identification and intervention reduced the achievable burden reduction. Burden Reduction (%) 250 100 A delay of 10 weeks was added onto the previous response scenario. Intervention coverage is unchanged from the base case. 200 150 90% 50 Response Radius (km) 70% 100 50% 30% 50 10% 0.1 1.0 10 100 Surveillance Rate (%)

Increased Vaccine Need (%) Vaccine demand provides bounds on outbreak response strategies Response increases vaccine need; at fixed burden reduction, improved surveillance tended toward more efficient vaccine usage. The tendency was stronger for greater effects. 16 -90% Burden Reduction Less surveillance at a given burden implies a larger response radius. Vaccine need is relative to baseline SIAs usage: 18M doses / year. As a limiting case, zero burden reduction has zero vaccine need. -80% 12 -70% -60% 8 Increased Vaccine Need (%) -50% -40% 4 -30% -20% -10% 0.1 1.0 10 100 Surveillance Rate (%)

Exploring proactively triggering SIAs based on known susceptibility Locally focused SIAs based on levels of under-five susceptibility guarantee burden reduction, but tend to be less dose efficient than a rapid response to observed incidence. 100 20 80 15 60 Proactive Burden Reduction (%) 10 Increased Vaccine Need (%) 40 Reactive 5 20 20 30 40 50 60 20 40 60 80 100 Under-Five Susceptibility Threshold (%) Burden Reduction (%)

Embedded Python scripts allow highly customized response strategies EMOD and Python run separate, concurrent processes, exchanging data as needed. Response planning occurs in Python, using a subset of the EMOD simulation data. Changing logic does not require rebuilding the EMOD executable. Plans can leverage Python module libraries such as scikit-learn. Start EMOD Python Start Python Update EMOD Simulation Update Python? Update risk mapping model Y N Plan reactive response

Acknowledgements National Primary Health Development Agency and NCDC (Ministry of Health of Nigeria) World Health Organization Nigeria office World Health Organization Headquarters Funding by Bill and Melinda Gates through the Global Good Fund ** Figure not to scale