Zika

Zika Research

Zika Virus

Zika virus (ZIKV) is an arbovirus belonging to the family Flaviviridae, and is closely related to the dengue, yellow fever, Japanese encephalitis, and West Nile viruses. It was first isolated from a monkey in the Zika forest of Uganda in 1947. In 1948, the second isolation was made from Aedes africanus mosquitoes in the same forest. Although earlier studies have suggested the possibility of human infection, before 2007, ZIKV rarely caused recognized 'spillover' infections in humans, even in highly enzootic areas. Since 1951, human ZIKV infections have been reported in African countries including Uganda, Nigeria, Senegal, and Gabon, and parts of South Asia including India, Malaysia, and Indonesia. In 2007, the first documented ZIKV outbreak was reported from Yap State, Federated States of Micronesia. French Polynesia also reported an outbreak with 28,000 estimated cases in 2013. Since April 2015, Brazil has been experiencing a ZIKV outbreak. The outbreak has subsequently spread to other countries in South America, Central America, and the Caribbean.

ZIKV is primarily transmitted by the bites of infectious Aedes mosquitoes, especially Aedes aegypti and Aedes albopictus. Non-vector borne transmissions, though rare, have also been reported. Symptoms of ZIKV infection are usually mild and can include fever, rash, conjunctivitis, and retro-orbital pain. The ZIKV infection is difficult to detect since its symptoms are similar to those of dengue and chikungunya. ZIKV has recently been connected to microcephaly in infants born to mothers infected with ZIKV during pregnancy. It is also suspected to be linked to Guillain-Barré Syndrome, a muscle weakness caused by the immune system damaging the peripheral nervous system.

Aedes mosquito, the vector of: Zika fever, Dengue fever, Chikungunya, Yellow fever, Venezuelan equine, encephalitis virus, La Crosse encephalitis

Anopheles mosquito, the vector of: Malaria

Culex mosquito, the vector of: West Nile fever, Japanese encephalitis, Saint Louis encephalitis, Western equine encephalitis virus

A Digital Disease Surveillance System: Analyzing Strategies for Containing the Zika Outbreak

The Zika virus (ZIKV) outbreak in South American countries and its potential association with microcephaly in newborns and Guillain-Barré Syndrome led the World Health Organization to declare a Public Health Emergency of International Concern. To understand the ZIKV disease dynamics and evaluate the effectiveness of different containment strategies, we have designed a compartmental model with a vector-host structure for ZIKV. The model utilizes logistic growth in human population and dynamic growth in vector population. Using this model, we derive the basic reproduction number to gain insight on containment strategies. We contrast the impact and influence of different parameters on the virus trend and outbreak spread. We also evaluate different containment strategies and their combination effects to achieve early containment by minimizing total infections. The model and results can help decision makers select and invest in the strategies most effective to combat the infection spread. The decision-support tool demonstrates the importance of “digital disease surveillance” in response to waves of epidemics including ZIKV, Dengue, Ebola and cholera.

We model the transmission of ZIKV based on SEIR compartmental model for the host and SEI compartmental model for the vectors, using logistic growth in human population and dynamic vector population.

We experiment containment of the outbreak by introducing interventions:

Reduce biting rate by avoiding mosquito bites, using insect repellents, wearing long-sleeved clothes and long pants, and using air conditioning and window/door screens to keep mosquitoes outside.
Reduce adult female vector population by widely applying insecticides in areas with high (infectious) mosquito population or
Increase adult female vector mortality by introducing genetically modified Aedes mosquitoes, which will cause its offspring to die by reducing larval survival rate and adult longevity.

To validate the performance of the model and investigate the effectiveness of containment strategies, we perform sensitivity and scenario analysis using the 2015 ZIKV data. Between January 2015 to July 2015, a total of 364 suspected cases were reported in Rio de Janeiro State. By November, the outbreak was contained with approximately 440 infection cases. We fit the model parameters using the data from this regional outbreak and contrast the effectiveness of different intervention method at different levels (see Figure 2). Our model covers the period January 2015 to November 2015. We assume strategies are implemented on May 1, 2015.

Figure 1. Transmission Model *Stage transition diagram for hosts and vectors and their transmission interplay. The dashed lines indicate transition associated with bites.

Figure 2a shows that reducing biting rate by a mere 20% will lead to an early containment by August 2015 (90 days after implementation) with 270 total infections (39% reduction). The containment is achieved rapidly (< 60 days after implementation) with total infection under 200 when biting rate is reduced by at least 40%. This proves that reducing biting rate is highly effective. This strategy is also relatively easy to implement.

Figure 2b shows that increasing the vector mortality rate by 20% will lead to an August containment with total infection of 282 (36% reduction). Containment will be achieved instantly with no more than 220 infections if the mortality rate is increased by at least 40%. While this strategy can be achieved by introducing genetically modified mosquitoes to the environment, it is difficult to realize and economically inferior when compared to other strategies.

Figure 2c shows that the larvae carrying capacity has to be reduced by 80% in order for total infection to reduce to 330 (26% reduction) with containment achieved by October. Nonetheless, examining and clearing the water ponds and humid areas where the larvae live remains an important strategy for public health protection.

Figure 2d shows that reducing vector population not only affects the number of infections at containment, it also changes the trend of the outbreak. Specifically, reducing total adult vector population by 20% would postpone the outbreak by a month and reduce total infection to 334. Containment can be rapid when reduction is targeted at 80% (total infection reduces to 130). Figures 3c and 3d highlight that reducing adult population is more effective, though applying insecticide widely may come with numerous environmental and health issues.

All four strategies are effective (in varying degrees) in containing the outbreak and reducing the overall infection. While findings for reducing biting rate and increasing vector mortality rate are similar for both populations (both are superior to other strategies), results for the larvae carrying capacity and reduction in vector population differ. These findings suggest environmental and demographic information should be considered when determining proper containment strategies. Our study also shows that reducing vector population early on can help delay the onset of the outbreak.

From Figure 3, we can observe that strategy S1= “reducing vector population by 30%, and reducing biting rate by 20%” has the same overall infection as strategy S2=“reducing vector population by 20%, reducing biting rate by 10%, and increasing vector mortality rate by 20%.” Both result in roughly 200 infections by containment. Similarly, strategy S3=“reducing vector population by 20%, and reducing biting rate by 10%” results in roughly 270 total number of infection, This is the same as, for xample, strategy S4=“reducing vector population by 10%, increasing vector mortality rate by 20%.” Policy makers strive for the lowest infection (darkest blue boxes) and can choose among these strategies that offer the best implementation tailor to their local needs.

Figure 3. This color-coded figure shows the outcome (total infections) resulted from different containment strategies. Each square corresponds to a combination strategy. For example, the box labeled “S0” represents the combined strategy of “reducing vector population by 20%, increasing vector mortality by 10%, and reducing biting rate by 5%.” We note the many boxes (strategies) that have the same color (total infection). This visualized pareto frontier offers an economic-decision-framework for policy makers. They can review results, contrast different outcomes, and select a strategy portfolio (with the minimum total infection) that is compatible with their local environment and regional demographics.

Combination strategies are both promising in practice and cost-effective in achieving early containment. The model provides a decision support framework for policy makers to estimate the cost-effectiveness for each prevention measure. Public health departments should select a strategy portfolio compatible with their local environment and regional demographics. In addition, the public should be educated and informed of ZIKV status. Population behaviors (protecting themselves by reducing biting, cleaning water ponds to rid of larvae, etc.) have demonstrably significant impact on containing and mitigating the outbreak.

The multiple components involving the dynamics of human and vector populations in this model allow flexibility in characterizing disease spread and performing strategic analysis. However, obtaining/determining all essential input parameters for this to be practical may prove difficult. The model can be simplified without diminishing its quality and rigor in disease dynamics and infection/containment outcome prediction. Such a model may be desirable when detailed input data are not readily available.

Zika Intervention Strategies for Puerto Rico

Puerto Rico reported its first case of Zika in December 2015. By January 2017, the number of confirmed cases has risen to 37,488 (2,962 are pregnant women). The estimated basic reproduction number for Zika is higher than that of Brazil since Puerto Rico has not implemented large-scale interventions as Brazil. There is a true return on virus spread containment if interventions are put in place rapidly.

Figure 4. (a) shows the effect of reducing biting rate (10% interval) on the number of affected newborns (blue is no reduction, orange is 10% reduction, grey is 20% reduction, yellow is 30% reduction, navy is 40% reduction, and green is 50% reduction). Zoomed in view along the x-axis (b), we observe that the total number of affected newborns is reduced by 97.8% (to 635) when the biting rate is reduced by 30%. Further containment can be achieved when the biting rate is reduced by at least 40%, resulting in fewer than 155 affected newborns.

Figure 5. (a) shows the effect of increasing vector mortality rate (10% interval). Figure 5. (b) shows that increasing vector mortality is slightly more effective than reducing the biting rate: the number of affected newborns will be reduced by 98.6% (to 410) when the vector mortality is increased by 20%.

Figure 6 demonstrates that decreasing vector population is least effective. To reduce the affected newborns by 97.8% (to 690), the vector population must be reduced by at least 50%.

Figure 7 contrasts the effectiveness of these three standalone strategies. Note the prediction against the reported number of cases (blue dots). We remark that the number of actual cases may be higher.

Figure 8

Figure 9

Figure 8 shows the effect of delay in pregnancy when no other interventions are introduced. The delay is relative to May 1, 2016. Delay pregnancy by fewer than 6 months do not significantly reduce the total number of affected newborns by the time the outbreak is contained. However, if the population delays the pregnancy by 9 months, the total number of affected newborns by containment is 15,480, almost half of the number when there is no delay. If the pregnancy is delayed by 1 year, the number of affected newborns will be 4,700 by containment, a 6-fold reduction. Individuals make their own choice based on their own personal belief and choice, and their own understanding of potential disease risk to the newborn.

From Figure 9, we provide a few example strategies here. Strategy S1= “reducing biting rate by 30%, increasing vector mortality rate by 14%, and reducing vector population by 28%” has the same effect as strategy S2=“reducing biting rate by 30%, increasing vector mortality rate by 28%, and reducing vector population by 13%.” They both result in 100 potentially affected newborns by containment. Similarly, the strategy S3=“reducing biting rate by 15%, increasing vector mortality rate by 23%, and reducing vector population by 10%” results in roughly 150 potentially affected newborns, which is the same as strategy S4=“reducing biting rate by 20%, increasing vector mortality rate by 10%, and reducing vector population by 22%.”

Persistently, the most effective way is to reduce the biting rate. Health departments should employ combination strategies while emphasizing the importance of self-protection against mosquito bites. The results enable policy makers to make portfolio investment on interventions sensibly and cost-effectively.

We realize the limited actual data linking birth defects to ZIKV, hence our model uses the total newborns to ZIKV infected women to establish the burden on healthcare system for health registry and monitoring for surveillance, proper diagnosis, and early intervention.

The model allows flexible design on disease characteristics, vector-human human-human behavior (stochastic nature on how they protect themselves, use of contraceptive, etc), recovery pattern (e.g., can incorporate population in which ZIKV found in semen after prolonged period), environmental factors, health risks, and intervention strategies. It can be adapted in real-time as new data is fed in.

The economy decision support engine returns estimated healthcare costs and economic burden, optimize worker resources and procedures needed for monitoring and registering. It can also incorporate clinical/environment/genomic components for machine learning predictive analytics for target diagnosis of susceptible population for early testing and intervention.

Surveillance of Vectors, Risk Assessment and Disease Spread Prediction

Surveillance, research, and control of mosquito-borne diseases require efficient methods for sampling mosquitoes. Mosquito traps have been used for sampling, biosurveillance and analysis of mosquito population, distribution, and disease spread. Below are 2016 biosurveillance data from mosquito traps (CDC light traps, CDC Gravid Traps, and BG Sentinel Traps) in Houston Texas showing the top two types of mosquitoes (Culex and Aedes genus).

Figure 10. 2016 distribution of Culex (left) and Aedes (right) genus from mosquito trap sampling in a county in Houston.

(a) (a) Mosquito Per Person

(b) Predicted Infection Rate

Figrue 11. Compartmental Disease Models Simulation Results.