Quantification of human-mosquito contact rate using surveys and its application in determining dengue viral transmission risk

Aedes-borne viral diseases, including dengue fever, chikungunya, and Zika, have been surging in incidence and spreading to new areas where their mosquito vectors thrive. To estimate viral transmission risks, availability of accurate local transmission parameters is essential. One of the most important parameters to determine infection risk is the human-mosquito contact rate. However, this rate has rarely been characterized due to the lack of a feasible research method. In this study, human-mosquito contact rates were evaluated in two study sites within the Greater New Orleans Region by asking a group of survey participants to estimate mosquito bites they experienced in the past 24 hours. The fraction of the mosquito bites attributed to Ae. aegypti or Ae. albopictus was estimated by human landing sampling. The results showed a significantly higher outdoor mosquito bite exposure than indoor exposure. The number of reported mosquito bites was positively correlated with the time that study participants spent outside during at-risk periods. There was also a significant effect of the study site on outdoor bite exposure, possibly because of the difference in the numbers of host-seeking mosquitoes. We use a mathematical dengue virus transmission model to estimate the transmission risks in the study areas based on local conditions. This compartmental model demonstrated how the observed difference in the human-Aedes contact rates in the two study sites would result in differential dengue transmission risks. This study highlights the practicality of using a survey to estimate human-mosquito contact rates and serves as a basis for future evaluations. Combined with the use of mathematical modeling, this innovative method may lead to more effective mosquito-borne pathogen prevention and control. Author summary Even though the human-mosquito contact rate is among the most important indicators of mosquito-borne viral transmission risk, it is rarely characterized in the field. Human Landing Capture is a gold standard method to quantify this rate, but it ignores variables such as human behaviors and lifestyles. In this study, we tested the feasibility of using surveys to quantify mosquito bite exposure in the Southern United States. The survey results, combined with mosquito species proportion data, were used to estimate the contact rate. These rates are key parameters used in mathematical models to determine transmission risks. We found that bite exposure occurred more often outside homes and people who spent more time outdoors in the evening and night had a higher exposure. Our model analysis shows that the human-mosquito contact rate is one of the most important parameters determining outbreak potential. Disease control programs should focus their efforts on reducing this rate in addition to the mosquito density. Future studies should test if the entomological contact rates described by surveys correlate with disease incidences or other entomological indices. This study highlights the importance of characterizing how vector-human contact rates may respond to changing human behaviors and environments.


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contact rate data hinders our progress in understanding how changing environments and human 80 behaviors will affect mosquito-borne virus transmission and emergence. We need to know how 81 often, and under what circumstances, humans are exposed to mosquito bites to plan effective 82 mitigation strategies.

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To date, only a few approaches have been used to approximate contact patterns in the 84 field. Human Landing Capture (HLC) is the traditional gold standard method to monitor human-85 vector contact patterns in malaria transmission [21,22]. This method involves human volunteers 86 collecting mosquitoes that land on them to feed, typically at night when Anopheles spp., the 87 vectors transmitting malaria, seek a blood meal. A well-designed HLC study could potentially 88 approximate the contact rate when humans are bitten by mosquitoes while sleeping. However,

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because Aedes spp. bite during the day when humans could actively interrupt or avoid mosquito 90 bites, this could result in a potential bias for the HLC estimates. The contact rate depends heavily 91 on housing infrastructure, human behaviors, and lifestyle differences that cannot be captured 92 easily by an HLC experiment [23][24][25].

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In this study, we approximated the contact rates between Aedes spp. and humans in the 94 Greater New Orleans Region using a questionnaire-based survey and a small-scale HLC 95 experiment. A short questionnaire in the form of door hangers was used to ask research 96 participants about the frequency and location of mosquito bite exposures in the past 24 hours. An

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HLC study was performed to determine the proportion of mosquito bites that belong to either Ae.
98 aegypti or Ae. albopictus. Next, the contact rates between humans and the Aedes species were 99 calculated. Finally, a deterministic compartmental SEIR (Susceptible, Exposed, Infected, 100 Recovered) model describing DENV transmission by Ae. aegypti and Ae. albopictus was used to 101 compare how the model predictions depend on the locally characterized human-mosquito contact 102 rates from two distinct locations.

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The ultimate goals of this study were: 1) to test the feasibility of using questionnaire-104 based surveys to quantify human-mosquito contact rates, 2) to understand how environmental 105 factors and human behaviors may impact mosquito bite exposure, and 3) to model how changes 106 in human-mosquito contact rates impact pathogen transmission outcomes.

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Study sites and survey methods 110 We designed two questionnaires in the form of door hangers. We intentionally designed

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In the second survey, only two study sites, ORL and TAM, were included. The study 129 period was from April to August 2017. In each month, 4 street blocks from each study site were 130 randomly selected, without replacement, to receive the questionnaires on Sundays, and another 4 131 blocks on either Wednesdays or Thursdays. The questionnaires were distributed to all addresses 132 in the chosen blocks and retrieved back the next day. No identifying information or addresses 133 were collected from the study subjects, and the Tulane University's Internal Review Board (IRB) 134 approved the full-review exempt status of both surveys (IRB reference number: 16-923467E).

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The ORL site was in an urban environment close to New Orleans city's downtown area.

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Compared to the other two study sites, ORL's residents were younger and lived in a smaller

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HLC locations were shaded outdoor areas. The collector was seated on a chair with the legs 154 exposed from the shoes up to the knees, and the lower arms were exposed from the elbows down.

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The human population was divided into 4 compartments: susceptible (S h ), exposed (E h ),

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infectious (I h ), and recovered/immune (R h ). The Ae. aegypti mosquito population was divided into 207 3 compartments: susceptible (S g ), exposed (E g ), and infectious (I g ). The Ae. albopictus mosquito 208 population was also divided into 3 compartments: susceptible (S b ), exposed (E b ), and infectious 209 (I b ). The total population sizes for Ae. aegypti, Ae. albopictus and humans were N g = S g + E g + I g ,

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When a susceptible Ae. aegypti mosquito bit humans at a biting rate of B g /N g (bites per 228 mosquito per day), there was a probability I h /N h that the persons being bitten were infectious. If the person was infectious, then the biting Ae. aegypti mosquito in the class S g became infected 230 with a probability β g and moved to the exposed class E g . After an average extrinsic incubation 231 period 1/ν g days, the mosquito advanced to the infectious class I g . Similarly, when a susceptible 232 Ae. albopictus mosquito bit humans at a biting rate of B b /N b , there is a probability I h /N h that the 233 persons were infectious and a probability β b that the mosquito became infected and advanced to 234 the exposed class E b . After an extrinsic incubation period 1/ν b days, the Ae. albopictus mosquito 235 advanced to the infectious class I b . Both mosquito species remained infectious for life.

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Female mosquitoes entered the susceptible class through recruitment from the pupal 237 stage. The recruitment term for mosquitoes was proportional to the egg-laying rate of adult 238 female mosquitoes and accounted for the hatching rate of eggs and survival of larvae and pupae.

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The aquatic stages were not explicitly included in the model and were approximated by a density-240 dependent recruitment (birth) rate. We assumed that all adult female Ae. aegypti and Ae.

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albopictus mosquitoes had the same per capita natural death rate μ g and μ b , respectively . In this 242 model, dengue infection did not affect the mosquito death rate or biting rate.

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Our ordinary differential compartmental equations modeling dengue transmission were: The female Ae. aegypti and Ae. albopictus recruitment rates were:

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Here, Ψ g and Ψ b were the per capita natural birth rates of female Ae. aegypti and Ae.
260 albopictus, respectively. In the absence of density dependence, r g and r b were the intrinsic growth 261 rates of female Ae. aegypti and Ae. albopictus, respectively, where r g = Ψ g -μ g and r b = Ψ b -μ b .

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K g and K b were the carrying capacity of the female Ae. aegypti and Ae. albopictus, respectively, 263 in the area of interest.

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The force of infection from mosquitoes to humans (λ h ) was the product of the average 265 number of bites a person received from mosquitoes per day (B g /N h and B b /N h ), the probability 266 that the mosquito was infectious (I g /N g and I b /N b ), and the probability of virus transmission from 267 the biting and infectious mosquito to the human (β h ), The force of infection from humans to Ae. aegypti and to Ae. albopictus (λ g and λ b, 270 respectively) were the product of the number of bites per mosquito per day (B g /N g and B b /N b , 271 respectively), the probability that the bitten human was infectious (I h /N h ), and the probability of 272 pathogen transmission from an infected human to the biting mosquito (β g and β b , respectively).
The contact rates of humans and Ae. aegypti (B g ) or Ae. albopictus (B b ) were obtained 277 from this study. Other parameters were obtained from other sources (Table 1). 278 If H 0 was the human population size, then, the number of mosquito bites from mosquito 287 species v that all humans in the population received per day (or the contact rate) is The basic reproductive number (R 0 ) 290 The calculations and model analyses were done in MATLAB R2018a (version 9.4.0).

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The model outcomes of interest were 1) the initial rate of disease spread by evaluating the basic 292 reproduction number (R 0 ) and 2) the initial transient disease dynamics by evaluating the timing 293 and magnitude of the first epidemic peak. Disease-free equilibrium points are steady-state 294 solutions where there is no disease; i.e., no exposed or infectious individuals for both humans and , then the model for dengue transmission 296 had exactly one disease-free equilibrium point, X dfe = (H 0 , 0, 0, 0, K g , 0, 0, K b , 0, 0), with no 297 disease in the population.

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In a homogeneously mixed population, the basic reproduction number (R 0 ) is the 299 expected number of secondary infections that one infectious individual would cause over the 300 duration of the infectious period in a fully susceptible population [35]. From this definition, it can 301 be logically interpreted that when R 0 < 1, each infectious individual produces less than one new 302 infected individual on average and the pathogen transmission 'dies out' from the population.

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Conversely, if R 0 > 1, the pathogen is able to invade the susceptible population.

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The next generation operator approach was used to calculate R 0 [36]. The description of 305 the calculation of R 0 using the next generation operator is described in detail in Appendix A,

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which resulted in R 0 expression: and The basic reproduction number R 0 in (9) can be expressed in terms of these quantities as For vector-borne viral transmission between two humans, two stages of the transmission 327 process are involved: the transmission from human "A" to mosquito "B" (generation 1), and then 328 from mosquito "B" to another human "C" (generation 2). The number of mosquitoes "B" caused 329 by an infectious human "A" is R bh (or R gh ), and the number of humans "C" caused by each 330 infectious mosquito "B" is R hb (or R hg ). After two generations, the total number of secondary 331 human-to-human cases for both mosquito species is R hg R gh + R hb R bh . Therefore, the basic 332 reproductive number (R 0 ), which characterizes the number of cases in one generation, is the 333 geometric average of the cases in two generations, that is . are valid only at a small range around the parameter baseline values.

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In the extended sensitivity analysis, the responses of R 0 to the variations in each 349 parameter of interest are calculated over the entire possible range of that parameter (Table 1)

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The preliminary results suggested variations between study sites. Research participants in 371 JEF reported higher exposure to mosquito bites than research participants in ORL and TAM. In

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TAM, around 40% of research participants indicated that they did not receive any mosquito bites 373 in the past 7 days. While in ORL, 38% of research participants chose "1-5" bites in the past 7 374 days. In JEF, equal proportions (23%) of research participants reported being bitten more than 10 375 times, 5-10 times, 1-5 times, and none in the past 7 days.

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When asked how often they experienced mosquito bites inside of their homes, 19% of 377 research participants from JEF chose "often" as the answer, higher than the other two study sites 378 (both were <5%). In all study sites, the place where people most often experienced outdoor 379 mosquito bites was around their homes (78%, 72%, 56% for TAM, JEF, and ORL, respectively).

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In ORL, "public space" was also reported as a place where people most often experienced 381 mosquito bites (32%).

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Of these, one person was between 18-25 years old, 38 were between 26-40 years old, 78 were 394 between 41-65 years old, 63 were more than 65 years old, and 5 failed to indicate their age range.

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Overall, the reported numbers of mosquito bites that occurred outdoors and indoors

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Given the baseline value of human-mosquito contact rate in ORL, the number of infected 506 Ae. aegypti at its peak was 4,647. This is higher than infected Ae. albopictus, where their number 507 at the peak was 182 (Fig 4). When using the maximum value of human-mosquito contact rate in

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Local sensitivity analysis 514 The local sensitivity indices of R 0 with respect to model parameters are shown in Table 3.

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For both transmission scenarios in ORL and TAM, the R 0 is most sensitive to 1) Ae. aegypti-516 human contact rate (B g ), 2) the probability of DENV transmission from mosquito to human given

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Therefore, as the contact rate between mosquito and human increases, the R 0 also increases. On 527 the contrary, the sensitivity indices of R 0 with respect to γ h , evaluated at their baseline values, are 528 negative. As a result, as the human recovery rate increases (i.e. viremic period decreases), the R 0 529 decreases. Another observation is the negative value of the sensitivity indices of R 0 with respect 530 to the mosquito carrying capacity (both K g and K b ), evaluated at their baseline values. This can be 531 interpreted that as the mosquito carrying capacity increases, the R 0 decreases. The mathematical 532 explanation for this unexpected relationship is discussed in the Discussion section.

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The relative ranking of the parameter importance was almost the same between the two 534 scenarios (Table 3). The only exception is that B b , or Ae. albopictus-human contact rate, becomes 535 relatively less important at determining R 0 in the ORL scenario compared to TAM. This results 536 from the assumption that Ae. Albopictus has a lower vector competence than Ae. aegypti, and Ae.

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aegypti-human has a higher contact rate in the ORL.

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Extended sensitivity analysis 539 The extended sensitivity analysis plots of R 0 with respect to the mosquito-human contact 540 rate for the transmission scenario in ORL are shown in Figure 6. The extended sensitivity analysis 541 plots of R 0 to other selected model parameters for ORL and TAM are shown in Supplementary 542 Figure 4 and 5, respectively.  rate, while holding other parameters at their baselines, will cause R 0 to decrease. However, this 555 relationship is not linear; as the contact rate decreases, the slope becomes smaller. That is, the 556 reduction in human-mosquito contact rate, when focused on only one vector species at a time,

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becomes less effective at reducing R 0 when the contact rate is already small. In fact, in the ORL 558 scenario, reducing the contact rate between humans and only one vector species at a time will fail 559 to reduce R 0 below 1. This is because the contact rate between humans and the other vector 560 species is high enough to maintain the transmission.

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Next, consider the bottom graph in Fig 5, which shows how R 0 changes in response to the 562 changes in both B g and B b simultaneously, while holding other parameters at their baseline 563 values. In this case, the reduction of both B g and B b at the same time below certain threshold 564 values will result in R 0 < 1.

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Global sensitivity analysis 566 Figure 7 shows the distribution of R 0 calculated from combinations of model parameter 567 values, which were sampled uniformly and independently within their possible ranges. The R 0 568 distribution for the ORL scenario was wider at the base and had a longer tailed distribution,

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indicating that there was a higher variation in the outcomes. The percentage of scenarios (or the 570 combinations of parameter values) that resulted in an R 0 > 1 indicated how likely DENV was to 571 spread in either location. In the ORL case, 74.52% of scenarios resulted in an R0>1. In TAM, 572 68.80% of scenarios resulted in an R0>1. As such, ORL was more receptive to an initial outbreak 573 of DENV than TAM.

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Mosquito bite exposure was investigated using a questionnaire survey to ask research 582 participants about their past experience receiving mosquito bites. We found that the mosquito bite 583 exposure on research participants occurred more frequently in the outdoors than indoors in both 584 study sites. The location that research participants most often reported being exposed to mosquito 585 bites was around their homes. We quantified the correlation between the reported bite number

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However, there was a higher discrepancy between the reported bites and the trap count at the 612 lower trap count.

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The HLC data from this study indicated that there were higher numbers of host-seeking 614 mosquitoes in ORL than in TAM, and more in the evening than in the morning. Even though this 615 study was not designed to compare the bite survey to HLC, the observations from both methods 616 were congruous. For example, the higher reported mosquito bite exposure in ORL mirrored the 617 higher number of host-seeking mosquitoes in that site, compared to TAM. In addition, the 618 correlation between the reported outdoors time and the amount of mosquito bites was found only 619 in the evening and nighttime, but not in the morning. This finding was consistent with our HLC 620 data and other studies, which found higher numbers of host-seeking Ae. aegypti in the evenings 621 than in the mornings [24,41]. Future study is needed to investigate the correlation between the 622 reported bite exposure level from surveys and the number of landed mosquitoes from HLC 623 experiments.

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Our model analysis showed that the human-mosquito contact rate played an important 625 role in determining contrasting outcomes in dengue transmission simulated in the two study sites.

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The local sensitivity indices indicated that the contact rate between humans and Ae. aegypti was 627 the most important parameter determining the R 0 , and was more important that the contact rate

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Computational uncertainties are unavoidable in predicting the dynamics of an epidemic.

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The baseline model parameters in Table 1, together with the human-mosquito contact rates despite the highly receptive condition, the probability of a DENV outbreak could be lower due to 711 its low vulnerability.

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In conclusion, we found that the use of a questionnaire-based survey is a feasible method 736 Acknowledgements

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We are grateful to all the survey participants. We would like to thank the Department of 738 Tropical Medicine, Tulane University for support. This study was partially supported by the NSF 739 award 1563531; the funding agencies had no involvement in study design, data analysis, or 740 decision to publish. by other means out of, and into each compartment, respectively.