Potential Zika virus spread within and beyond India
Bibliographic record
Abstract
As of 28 October 2018, 147 cases of Zika virus disease (ZVD) have been reported in Jaipur, the capital of Rajasthan state, India.1 Subsequently, as of 2 November 2018, a single case was reported in the neighbouring state of Gujarat and three additional cases were reported in the state of Madhya Pradesh, demonstrating national spread of ZVD and marking the largest reported outbreak of ZVD in Indian history.1 State health departments in India have mobilised hundreds of medical personnel to perform emergency screenings for ZVD.1 As a major tourist attraction for domestic and foreign visitors, the outbreak in Jaipur presents a high risk of Zika virus exportation. To anticipate the potential spread of ZVD in the face of an ongoing outbreak in Jaipur, we determined temporally explicit air travel connectivity with Jaipur and the corresponding seasonal environmental suitability for Zika virus transmission in domestic and international destination cities. We ranked destination cities based on their arriving volume of travellers on commercial flights from Rajasthan for November, December and January using passenger-level, full-route, flight itinerary data from the International Air Transport Association (IATA) for the year 2017. We delineated suitability for transmission of Zika virus in India and Southeast Asia using distribution models of the virus’s primary mosquito vector Aedes aegypti and secondary vector Aedes albopictus limited by the well-characterised temperature thresholds for the genetically similar dengue virus for November, December and January.2 Each month, top ranking domestic and international cities were subsequently filtered by connectivity to include only those cities located within 200 km of areas suitable for Zika virus transmission. Over this 3-month period, 326 cities that were within 200 km of areas suitable for Zika virus transmission received a total of 740,232 passengers from Rajasthan (summarised for December in Figure 1). Of these passengers, approximately 94% travelled to cities within India (n = 696,753). Mumbai received the most passengers (>24%), with Delhi, Bengaluru and Kolkata ranking second, third and fourth, respectively, across all 3 months. Bangkok, Muscat and Singapore were the only international cities ranked in the top 20 destinations. Number of passengers arriving from Rajasthan state (highlighted in red) by air for cities within 200 km of any Zika suitable area estimated for December. Proportion of total outbound passengers from Rajasthan provided in parentheses. Case counts for Jaipur are reported as of 2 November 2018.1 Given the abundance of regions that are predicted to support Zika virus transmission and have large populations with limited previous exposure, and thus limited immunity, to Zika virus, Indian cities and countries with close international connections to Jaipur should prepare for potential importations of Zika virus. Our results suggest a greater risk of domestic spread from Jaipur within India in the upcoming months but relatively lower potential for international exportation and spread. Notably, the city of Chennai may be especially vulnerable given relatively high connectivity to Rajasthan, a large urban population (>7 million), and conditions conducive to year-round transmission of Zika virus via Ae. aegypti. If not controlled, the ZVD outbreak in Jaipur could have far-reaching consequences,3,4 and public health and clinical personnel in domestic and global areas connected to the current epidemic should remain vigilant for possible importation of ZVD cases. We thank Kieran Petrasek, Hernan Acosta, Deepit Bhatia, Andrea Thomas-Bachli, Mariana Torres, and Ashleigh Tuite for their contributions to the study design and data collection for this work. Centers for Disease Control and Prevention (Grant no. USG CK000433-01).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".