Zika Research at HealthMap

In early January, the HealthMap Zika page – which aggregates media alerts across multiple platforms about Zika virus and Zika fever – went live to the public. By design, these digital surveillance data – which include information on both case counts and case locations, etc. – are open and available for anyone to access and use.

Since going live, we’ve been able to do a couple of pretty cool things with the data that we’ve been collecting:

  1.  To date, confirmed case counts over time have not been reported by the WHO, PAHO, or by public health agencies in affected countries. With this in mind, our first priority was building out (and regularly updating) a cumulative epidemiological curve (epicurve) based on reported cases counts captured by the HealthMap surveillance system:

    epicurve_2.7.16
    As is noted in the pink textbox, these case counts are likely just the tip of the iceberg; because Zika fever is generally mild, infected individuals often don’t seek care. However, epicurves like this one – even though they only capture confirmed and reported cases – lend important insights into the transmission dynamics of an ongoing outbreak (as well as media interest in it). Here, we see that the epicurve is L-shaped, which indicates that there’s a rapid increase in both disease transmission and media awareness, perhaps due to recent evidence linking vertical transmission of Zika virus to microcephaly in newborn babies.

  2. In addition to creating the above epicurve as a resource for the general public and researchers alike, our paper “Estimating a feasible serial interval range for Zika fever” went live on the Bulletin of the World Health Organization’s Zika Open webpage earlier this week. As is clear from the Methods section, this study was also developed using data collected via the HealthMap digital disease surveillance system.

    The paper has generated a lot of positive attention on Twitter, especially among disease surveillance and modeling geeks:

    In lay terms, the serial interval of a given disease is defined as the average time between two consecutive cases in a chain of transmission. Using a combination of life cycle modeling and digital disease data, we tried to determine a feasible range for the serial interval associated with Zika fever in Central and South America. Our finding? 10 to 24 days for vector-borne transmission*.

    Knowing this range is important for a number of reasons, which are nicely outlined in this 2003 article by Dr. Paul Fine. More explicitly, in the realms of Zika fever surveillance and modeling:

    i. It lets us know how long to amp up surveillance in a given community (e.g. viable vectors present) after an index case emerges. If 24 days pass (or 48, just to be safe) without a new Zika infection, we can reasonably declare the community as  “Zika-free”**. (Surveillance is expensive, so you don’t want to be doing it for longer than necessary.)
    ii. It is an important parameter for a variety of different disease modeling techniques. (This includes both descriptive and mechanistic approaches, both of which are used to inform resource allocation and decision-making during outbreaks – often via projection of disease burden in the coming weeks.)

    *Additional serial interval estimates may be necessary in the future for other modes of transmission (e.g. sexual, vertical, blood transfusion)
    **Under the assumption that other modes of transmission are not in effect

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2 thoughts on “Zika Research at HealthMap

  1. Pingback: Zika Research at HealthMap | Ned Hamson's Second Line View of the News

  2. Pingback: Zika Open & DDD | Mens et Manus

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