Digital Disease Detection: MERS, Ebola, and Measles. Maimuna S. Majumder

Transcription

1 Digital Disease Detection: MERS, Ebola, and Measles Maimuna S. Majumder

2 2 Roadmap Digital Disease Detection What is DDD? Strengths & Weaknesses Common Uses Outbreak Dynamics Case Fatality Rate Mortality Risk Factors Basic Reproductive Number Effective (or Observed) Reproductive Number Percent Immune (or Vaccinated) Case Studies EID: South Korean MERS Outbreak PLOS Outbreaks: West African Ebola Outbreak JAMA Pediatrics: Disneyland Measles Outbreak Discussion & Questions Extra Slides: Visualization

3 Digital Disease Detection 3

4 4 What is DDD? 1. Social, news media (HealthMap); search data (Google search) 2. Quasi-DDD: Automated translation + digitized official documents

5 5 Strengths & Weaknesses Strengths Faster than traditional surveillance Provides insight into local context when boots on the ground aren t feasible Weaknesses Highly contingent on internet penetration Can be challenging to extract signal from noise Disease incidence vs. disease interest Example: Google search trends

6 6 Common Uses 1. Distance appraisal of interventions, aggravating events 2. Near real-time assessment of outbreak dynamics

7 Outbreak Dynamics 7

8 8 Case Fatality Rate Strengths: Provides an assessment of how deadly an outbreak of a given disease is Weaknesses: Varies over time and must be continuously recalculated Must be continuously recalculated Not a universal measure and masks localized CFR = N(Deaths) N(Cases) Definition: Probability of death given infection

9 9 Mortality Risk Factors Definition: Characteristics (like demographics) that increase probability of death from a given disease Methods: Univariate & multivariate logistic regression (with outcome as output) Strengths: Can inform risk-based care delivery, thus improving outcomes Weaknesses: Highly dependent on data quality Individual-level line list data can be rare in the DDD universe due to issues of patient protection, privacy Biases of self-reported information

10 10 Basic Reproductive Number, R 0 Definition: Average number of secondary infections caused by a case in a ~fully susceptible community Methods: Deterministic & stochastic common, but descriptive is often preferred in DDD Strengths: Serves as a quick and dirty measure of potential transmissibility for an outbreak of a given disease in a (toy) scenario in which susceptibility does not wane Weaknesses: Average masks heterogeneity of infectiousness and susceptibility Cases may cause wildly different numbers of secondary infections in a fully susceptible community Ranges are better; distributions are best

11 11 Effective Reproductive Number, R E Definition: Average number of secondary infections caused by a case in a partly susceptible community (also known as observed reproductive number, R Obs ) Methods: Deterministic & stochastic common, but descriptive is often preferred in DDD Strengths: Serves as a quick and dirty measure of actual transmissibility for an outbreak of a given disease in as susceptibility wanes Weaknesses: Still an average, just like R 0 Cases may cause wildly different numbers of secondary infections in a partly susceptible community Ranges are better; distributions are best

12 12 Percent Immune Community, I Strengths: Provides an assessment of what percentage of the community is no longer vulnerable to infection Weaknesses: Varies over time and space Must be continuously recalculated Not a universal measure and masks localized heterogeneity I =1 R E R 0 Definition: Fraction of the community that is not susceptible

13 Case Studies 13

14 EID: South Korean MERS Outbreak 14

15 15 Outbreak Background Largest MERS outbreak outside of Saudi Arabia Prompted by exportation event from Middle East Aided by culture of hospital shopping Extreme superspreading, nosocomial amplification Number of Cases May May May-15 Recovered Deceased 26-May May-15 5-Jun Jun Jun-15 Date of Symptom Onset 20-Jun Jun Jun-15 5-Jul-15

16 Q: What were the risk factors for mortality? 16

17 17 Data & Methods Line list curated from digital case reports via South Korean Ministry of Health (in Korean) and the World Health Organization (in English), N = 159 Five risk factors (covariates): sex, age, concurrent health condition status, health care worker status, time from onset to diagnosis Logistic regression with outcome (deceased vs. recovered) as output Univariate to explore possible relationships Multivariate to control for confounders

18 18 Findings Statistically significant risk factors for mortality Older age Preexisting concurrent health conditions Considerably smaller CFR (~20%) than in Saudi Arabia (~40%), where MERS is endemic Differential prevalence of risk factors Treatment, surveillance disparities

19 PLOS Outbreaks: West African Ebola Outbreak 19

20 20 Outbreak Background Largest documented outbreak of Ebola virus disease in recorded history Zoonotic emergence in Guinea, December 2013 Geographic spread via ease of transportation and human-to-human transmission Exportations to several other countries, including the US Long-term sustained transmission only in 3 countries, with largest case counts in Liberia and Sierra Leone

21 21 Q: How did interventions and aggravating events impact transmission dynamics in Liberia and Sierra Leone?

22 22 Data & Methods Case counts over time via the World Health Organization, media events via HealthMap Media events categorized into interventions (minimizing contact, strengthening healthcare, providing education) and aggravating events Incidence decay and exponential adjustment model used to describe observed (effective) reproductive number (R Obs ) over time

23 23 Findings Aggravating events at t correlated with increase in R Obs at t+1 r 2 =.96 in Liberia r 2 =.52 in Sierra Leone Media events may improve accuracy of transmission dynamics modeling during outbreaks 2.5 Observed Reproductive Number Sierra Leone Observed Reproductive Number Liberia 1.6 Interventions Aggravating Events Interventions Aggravating Events 14-Apr-14 2-May May-14 7-Jun Jun Jul Jul Aug-14 5-Sep Sep Oct-14 Minimizing Contact Strengthening Healthcare Providing Education Aggravating Event

24 JAMA Pediatrics: US Measles Outbreak 24

25 25 Outbreak Background Rapid growth of measles in California, Prompted by an index case at Disneyland 80 cases in just 5 weeks, despite national and statewide 1 dose vaccination rates of >90% Cumulative Cases Dec Dec-14 Total Cumulative Cases 31-Dec-14 7-Jan-15 California Only 14-Jan Jan Jan-15

26 26 Q: What fraction of the affected population was vaccinated against measles, mumps, and rubella?

27 27 Data & Methods Case counts over time via HealthMap media alerts (and retrospectively corroborated by the California Department of Public Health) HealthMap system picked up case counts from media alerts faster than the Department of Public Health provided official data Early case counts via HealthMap matched the Department of Public Health counts closely Incidence decay and exponential adjustment model used to describe effective reproductive number (R E ) over time

28 28 Findings Vaccination rates among the affected population was likely suboptimal No higher than 86% and perhaps as low as 50% 96 99% vaccination rate is needed to achieve herd immunity and prevent future sustained transmission of measles May be due to increasing vaccine-hesitancy in the US Estimated MMR Vaccination Rates Estimated Effective Reproductive Number Basic Reproductive Number

29 Discussion & Questions 29

30 Extra Slides: Visualization 30

31 31 MERS in South Korea Extreme super-spreading Individual-level heterogeneity How does super-spreading impact assessments of R0? Would R0 be a useful transmission parameter to calculate here? What other parameters should be calculated? T Saey, MS Majumder, and S Egts for Science News Magazine

32 32 Ebola in West Africa Comparing scale of West African outbreak to outbreaks of the past Spatiotemporal heterogeneity How have sizes of outbreaks changed over time and space? What does this tell us about susceptibility and transmissibility during the West African outbreak? What about changes in detection and surveillance? MS Majumder for Significance Magazine West Africa * Uganda DRC 2012 Uganda 2012 Uganda 2011 DRC * Uganda DRC 2007* South Sudan 2004 Congo 2003* Congo * Gabon & Congo * Uganda Gabon * South Africa 1996* Gabon 1996* DRC 1995* Ivory Coast 1994 Gabon 1994* South Sudan 1979 DRC 1977* South Sudan 1976 DRC 1976* Guinea Sierra Leone Liberia Countries with Sustained Transmission Reported Cases of Ebola, Updated: Zaire ebolavirus [EBOV] outbreaks demarcated with [*]

33 33 Measles in the US Herd immunity: fraction of the community that must be immune to prevent sustained transmission Localized heterogeneity When is national-level vaccination a useful statistic? How do we define a community? At what geographic scale should vaccination rates be analyzed in the US? MS Majumder for HealthMap MEASLES: Vaccination Heterogeneity For a disease as contagious as measles, >95% of a given community needs to be vaccinated in order to confer herd immunity. 1 Because the national population-level MMR vaccination rate in the United States meets this benchmark, 2 some parents believe that their children are safe from the measles virus even if they haven t received the measles vaccine. Unfortunately, national population-level coverage statistics can give us a false sense of security. Community-level vaccination rates reveal heterogeneities in vaccine uptake that tell a more complicated story. COMMUNITY A Let s consider a population that consists of two communities: COMMUNITY B POPULATION LEGEND maiamajumder 100 persons, per circle Vaccinated, Community A Vaccinated, Community B Vaccinated, but ineffective Unvaccinated persons Among the 12,000 individuals in this semi-fictitious population, 95% are vaccinated but Community A has 99% coverage and Community B has 75%. Assuming 95% vaccine efficacy, 1 true immunization rates dip down to 94% and 71% respectively. Thus, while individuals who live in Community A are well-protected against measles, individuals who live in Community B are not. The population-level vaccination coverage statistic of 95% doesn t tell the whole story.* Bottom line? Though the national MMR vaccination rate is quite high in the United States, herd immunity is a community-level phenomenon not a national one. Community-level vaccination rates provide a clearer picture of individual risk, and each vaccine contributes towards conferring localized herd immunity. Simply put: if you can vaccinate, please do! NOTES & REFS *NOTE: Things get even trickier when we consider the fact that in reality the border between the two communities is probably pretty permeable, but we won t cover that here today. REFS: 1. Plans-Rubió P. Evaluation of the establishment of herd immunity in the population by means of serological surveys and vaccination coverage. Hum Vaccin Immunother. 2012;8(2): Seither R, Masalovich S, Knighton CL, Mellerson J, Singleton JA, Greby SM. Vaccination Coverage Among Children in Kindergarten. MMWR Morb Mortal Wkly Rep. 2014;63(41):