Ethics statement

This manuscript is a theoretical modeling exercise and no field collected human or entomological data was included. The modeling was supported under human use protocol NAMRU-62014.0028 approved by the NAMRU-6 ethic committee and the Loreto Regional Health Department.

Model framework

Our model builds on a previously published mathematical framework that describes where and when human-mosquito contacts occur based on fine-scale human and mosquito mobility [16]. In the original framework, parameters with set values were defined (S1 Table), then a set of houses, {f}, and larval development sites, {l}, were arranged on a disc. Each house was assigned a number of residents equal to 2 plus a Poisson random variable (λ = 3.5), creating a two-person minimum per household. In order to assign the numbers of mosquitoes/larvae at each house/larval site, mosquito movement and reproduction were simulated for a total of 200 time steps, with the first 100 acting as a burn-in period. Counts of mosquitoes and larvae at each location were averaged over the second 100 time steps, providing the ‘equilibrium’ values (Fig 1A). Poorly mixed mosquito movement was characterized by matrices L and F, giving the distance-based probabilities of an adult female mosquito moving from any house to any larval site, and vice versa (Fig 1A and S2 Table) [16].

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Fig 1. Diagram with setup of model framework for each scenario and simulation run.

(A) For each scenario, this model includes houses and larval sites in which mosquitoes breed and move with specific daily probabilities. (B) For each of 200 simulation runs, a random social network (SN) is generated for humans, defining their contacts at home and at other houses and further determining the human movement matrix (HM). (C) Diagram of our stochastic compartmental transmission model in which mosquitoes at the household level were modeled as SEI (with M subscripts) and humans as an individual-based SEIR (with H subscripts). The I (infectious) stage for humans was divided into five sub-stages, each with their infectiousness value, shown here with weighted arrows. Individuals can either progress to the next (I_Hi) infectious sub-stage or move straight to the (R_H) recovered stage based on a probability function. The probability of moving to the recovered stage is shown with weighted arrows (thicker arrows indicate higher weights).

https://doi.org/10.1371/journal.pcbi.1008627.g001

Rather than defining human mobility patterns based on distance (as in [16]), we generated a socially structured human mobility matrix for each of 200 simulation runs (Fig 1B). First, a random social network with household structure was constructed using the “configuration model” [39]. Each individual was assigned a number of “half-edges” (their degree) from a Poisson distribution with rate λ = 2.8, the mean number of residential locations visited in a data set described by Perkins et al. [32]. Based on human mobility data indicating that 14.7% of people do not regularly visit any houses (based on a 15 day monitoring period), a random sample of individuals predicted in the model to have a degree higher than 0 were reassigned a degree of 0 in order for the total modeled population to reach 15% of individuals with no half-edges and therefore no movement from their home [20]. Half-edges were then paired uniformly at random to form the edges of the social network, making sure there were no self-loops, multiple edges, or loops within houses (Fig 1B). This random network was represented as an |h|-by-|h| adjacency matrix SN, where |h| is the size of the set of hosts {h} (Table 1). A separate |h|-by-|f| presence/absence matrix, Homes, is constructed, where Homes[j,x] denotes whether or not the j^th host lives at the x^th residential site (Fig 1B and Table 1). Multiplying the SN and Homes matrices produced an |h|-by-|f| matrix, HM, denoting which residential sites an individual will visit based on their social network (note that this matrix was not presence/absence, as when the j^th host was socially connected to multiple individuals at the x^th residential site, HM[j,x] > 1) (Fig 1B and Table 1). This matrix was used to populate the human mobility matrix, H, which documented the proportion of time each host spent at each household (Table 1). Each host, j, spent 50% of their time at home (H[j, home] = 0.5) and divided the remaining 50% of their time into the houses visited in HM (When HM[j,x] > 1, as mentioned above, a proportionally larger amount of time was allocated at that residential location). For the 15% of individuals, j, who had no mobility outside their home H[j, home] = 1. As in the original model, each row of the H matrix described where a single host spent time and each column detailed all of the individuals spending time at a single household [16].

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Table 1. Definitions of parameters mentioned in text.

https://doi.org/10.1371/journal.pcbi.1008627.t001

Following the original model, each individual was also assigned a biting suitability score (which accounts for biting attractiveness, avoidance behavior, and defensive behavior) using a random exponential draw with rate based on empirical biting data [40]. Based on the mobility matrix, H, and biting suitability scores, an |f|-by-|h| matrix U was created to describe the distribution of mosquito bites on all individuals at each house, where each row gave the distribution of bites on all hosts at a single household and each column depicted the bites distributed on a single individual across all households (Table 1).

A stochastic transmission model was layered on top of this framework, which included a household-level SEI (susceptible, exposed, infectious) model for mosquitoes and an individual-based SEIR (susceptible, exposed, infectious, recovered) model for hosts (Fig 1C and S1 Fig). Individuals transitioned through one exposed (E) stage, based on pathogen latency of DENV in terms of feeding-cycle-length time steps. Hosts also transitioned through a maximum of five infectious (I₁ –I₅) sub-stages, until a stochastic transition into the recovered (R) stage. Rather than use a single set value for human infectiousness (as seen in Perkins et al.), values were chosen for each of these sub-stages (I₁ –I₅) based on data giving the mean daily probability of infection for mosquitoes after feeding on individuals with primary infections [16,38] (Fig 1C). For each 3-day infection time point in our model, we averaged these mean infectiousness values (Table 2). The updated transmission model also defined the first time step in the human infectiousness stage (I₁) as the “pre-symptomatic period” and all subsequent infectious time steps (I₂ –I₅) as the “symptomatic” period, where the pre-symptomatic period contributed to 25% of infectiousness for individuals who progressed through all five infectiousness stage (I₁ –I₅) before recovery [38]. Simulation outbreaks were initiated by moving a single human into the first infectious (I) stage.

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Table 2. Parameters that vary by infectiousness stage.

https://doi.org/10.1371/journal.pcbi.1008627.t002

Model simulations had discrete time steps to capture the length of a mosquito gonotrophic cycle (~3 days). During each time step, hosts would allocate their time at houses based on H. The mosquitoes at each house would take blood meals from possible hosts (one blood meal per mosquito) based on U matrix probabilities and move to a larval site based on L probabilities. Eggs were laid based on a Poisson distribution with mean equal to number of adult females at the site multiplied by average egg batch size. Adult mosquitoes then moved to a house searching for their next blood meal based on F probabilities. During each time step, mosquito larvae also progressed through 4 developmental stages based on site-specific density dependence until emerging into adult mosquitoes. For both mosquitoes and humans, each time step also accounted for progression through incubation (E), infectiousness (I), and (for hosts) recovery (R). Virus transmission occurred from infectious hosts to susceptible mosquitoes and from infectious mosquitoes to susceptible hosts. At the end of each time point, host mobility was changed for those hosts who were symptomatically infectiousness.

Host Mobility Changes: Two different scenarios were considered to examine mobility changes: (1) no symptomatic movement change and (2) movement change throughout symptomatic infection. For scenario (1) no changes were made to the mobility matrix. For scenario (2), host mobility changes occurred at each 3-day time step of symptomatic infection based on recently published data on human mobility throughout symptomatic DENV infection [31] (Table 2 and S2 Fig). As data from Schaber et al. [31] were grouped as days 1–3, 4–6, and 7–9 after symptom onset, they corresponded to the I₂, I₃, and I₄ stages here. When individuals were in the first three days after symptom onset, they were significantly more likely to spend all of their time at home and visit no other residential places. Accordingly, when an individual transitioned into symptomatic infection in the simulation (I₂), their movement was completely stopped (HM[j,] = 0) and all time was spent at home (HM[j, home] = 1). During days 4–6 after symptom onset (sub-stage I₃), individuals spent an average of 76% of time at home and visited approximately 1/3 of normally frequented places. On days 7–9 after symptom onset (sub-stage I₄) time at home and fraction of places being visited averaged 69% and 2/3, respectively. Therefore, we set the time at home to be 80% (70%) for the I₃ (I₄) stage and had individuals visiting 1/3 (2/3) of their originally frequented houses (Table 2). The order in which houses were added back into an individual’s movements in stages I₃ and I₄ was determined by random sample where a house’s probability of being chosen was weighted by its original HM value. This made it more likely that individuals would resume visiting houses where they were socially connected to multiple residents. When individuals reached the I₅ stage (days 10–12 after symptom onset), movement patterns and time at home were reset to original values (Table 2). At the end of each time step, once these movement changes were updated for all symptomatic individuals in the HM matrix, the H and U matrices were recalculated as described above.

The effect of the pre-symptomatic period was accounted for by including two more scenarios of interest: (1b) no movement changes and no pre-symptomatic period and (2b) movement change throughout symptomatic infection and no pre-symptomatic period. For these scenarios the first stage of infectiousness (I₁), the pre-symptomatic period, was removed and individuals became symptomatic immediately after the incubation period (E), with infectiousness and movements corresponding to the I₂ –I₅ stages.

In order to simplify the model and the effects of mobility change, two simplifications were made: inapparent cases were left out and mobility was completely halted on the first three days of symptoms. Two further versions of scenario (2) were created to ascertain the robustness of the model to changes in these parameters. The impact of symptomatic mobility change in the presence of inapparent and asymptomatic infections was examined with scenario (2c), where only 30% of individuals (chosen from a random binomial draw) had symptomatic infection with mobility change. In scenario (2d), we assessed the sensitivity of model outputs to mobility changes the first three days of symptoms. Rather than having mobility completely stop after symptom onset, it was decreased to the same level as days 4–6, where 80% of time was spent at home and 1/3 of original houses were visited (S3 Table).

Model Outputs: Perkins et al. created multiple metrics to explore how mobile hosts and mosquitoes contribute to pathogen dispersal [16]. Of particular interest was the matrix R, which corresponded to the concept of effective reproductive number. This matrix gave the probability that a primary infection in one host will result in a secondary infection in some other host, where summing each row provided the number of expected secondary infections arising from a single individual. The B matrix was also utilized to measure the expected number of bites per time step on each host at each blood-feeding habitat (house). Each row of B provided the number of expected bites on a single individual at all households and each column gave the expected number of bites occurring on all individuals at a single household during one time step. At the population level, dynamics were examined using the simulation outputs of cumulative number of infections at each time step and number of infectious hosts at each time step. We utilized these original metrics and created versions that accounted for mobility change.

The B matrix could be used as a way to examine heterogeneity in human-mosquito contact rates, not only across hosts/locations, but also throughout an individual’s infectiousness period. This metric was derived to account for multiple sources of heterogeneous biting, namely spatial variation in biting intensity, hosts’ allocation of time at a location, and relative biting attractiveness of hosts at that location. Spatial variation in biting intensity was included as a numeric vector with the number of expected bites per feeding cycle at each house (based on average number of new adult females emerging at each larval site and their subsequent mobility to nearby houses). Multiplying this numeric vector by the distribution of bites across hosts at each site (U), which account for the latter two factors of heterogeneous biting, results in the B matrix. Because this metric was based on the U matrix, and therefore affected by the human mobility matrix (H), a list of B matrices was created to measure biting pre-epidemic (with normal movements) and during each time step of infectiousness. Within each simulation, B_norm was calculated for all individuals before infection spread began. During disease spread, B_i[j,] was recorded for each host, j, at each infectiousness sub-stage (I₁ –I₅), i. This set of matrices gave us the expected number of mosquito bites on each host at each house throughout infectiousness/mobility changes (Table 1).

The previously-derived version of the R matrix, referred to as R_norm, measured the probability of host k receiving one or more secondary infectious bites arising from primary infectious host, j (Table 1). This accounts for the primary infectious host transmitting the virus to a susceptible mosquito (the primary infectious bite) and that newly infectious mosquito then transmitting the virus to a susceptible host (the secondary infectious bite). The R metric was slightly adjusted to account for time-step-specific infectiousness where with c_i values representing an individual’s time-step-specific infectiousness values. The V matrix gave the number of expected secondary bites on each host arising from primary bites on all other hosts over one time step, where each column provides the number of expected secondary bites received by a single individual from primary bites on all other hosts during one time step and each row described the number of expected secondary bites on all hosts from primary bites on a single individual. To derive the V matrix, we considered an individual, k, who received an expected number of (primary) bites per feeding cycle at each house based on the B matrix. The mosquitoes from this house then went on to make an expected number of (secondary) bites at all possible houses based on mosquito mobility matrices. These secondary bites were distributed among hosts according to the U matrix. Because the U matrix affected the V matrix, host mobility change was accounted for by creating a set of matrices, V_i, for each sub-stage of infectiousness (I₁ –I₅) (Table 1). When host j was infectious in the simulation, their V_i[j,] values were recorded for each I sub-stage (I₁ –I₅). At the end of the simulation run a matrix referred to as R_movement was created, where

In order to examine the importance of the location where the primary infectious bite occurs on host j, we also divided R_movement into two separate matrices, R_movement (home), and R_movement (other houses) (Table 1). This was done by calculating V_i(home) and V_i(other houses), which derived the number of expected secondary bites on each host arising from primary bites that occur at each time point of infectiousness, i, on all other hosts at their home and everywhere but their home, respectively. These V_i (home) and V_i (other houses) matrices were then used to derive R_movement (home) and R_movement (other houses), respectively. Similarly, R_norm was divided into R_norm (home) and R_norm (other houses) in order to compare the effect of where a primary infectious bite occurred when not accounting for mobility (Table 1).

A new metric that focused on the number of mosquitoes present in each individual’s home was also calculated. The number of mosquitoes in each individual’s home was recorded at the beginning of the simulation run (pre-epidemic) and at each time point of infectiousness for that individual. For each scenario a list was output with all of these metrics for each of 200 simulation runs.

Data analysis

Analysis of simulation outputs had three main objectives: determining the effects of disease-driven mobility changes on (1) population-level outbreak dynamics (e.g., total infections, timing of infection peak, length of epidemic), (2) individual-level onward transmission (i.e., the number of secondary infections arising from a single individual), and (3) individual-level human-mosquito contacts (i.e., the number of mosquito bites on a single host at each location during each stage of infectiousness).

For the first objective, determining the effects of mobility change on population-level disease dynamics, we compared four scenarios: no mobility change and no pre-symptomatic period (baseline); no mobility change and pre-symptomatic period; mobility change and no pre-symptomatic period; mobility change and pre-symptomatic period. The effect of mobility changes could be determined by comparing the “no mobility change” and “mobility change” scenarios. To determine the role of the pre-symptomatic period when mobility changes occur, we compared the difference in ‘mobility change, no pre-symptomatic’ and ‘mobility change, pre-symptomatic’ to the difference in ‘no mobility change, no pre-symptomatic’ and ‘no mobility change, pre-symptomatic’ in order to account for the effect of removing one period of infectiousness (the pre-symptomatic period).

The number of infectious hosts at each time step was used to calculate the maximum infection prevalence, the time to maximum prevalence, and the length of the epidemic (when the number of infectious hosts was 0 without increasing again). The cumulative number of infections at each time step was utilized to record the total percent of the population infected in an epidemic, as well the time point when the percent of cumulative infections reached 10% and 65% (representing the time the epidemic starts to take off and starts to slow down). For the remaining two objectives, we focused our analyses on the scenario where a pre-symptomatic period was present and mobility changes were occurring. In order to determine the effect of these mobility changes on onward transmission, the R_norm and R_movement matrices were utilized. Row sums of R_movement and R_norm gave the expected number of secondary infectious bites arising from all primary bites on an individual host either with or without accounting for movement changes. Similarly, row sums of R_movement(home), R_movement(other houses), R_norm(home), and R_norm(other houses) determined the expected secondary bites arising from an individual due to only primary bites at their home or only primary bites at other houses (with and without movement changes). The distributions of R_movement and R_norm values were compared and both absolute (R_{abs_change}) and relative change (R_{rel_change}) were calculated to examine how accounting for mobility affects an individual’s R-value, where and

Possible predictor variables for onward transmission were examined using generalized additive models (GAMs) [41]. Best-fit models were determined for R_norm, R_movement, R_movement(home), R_{rel_change}, and R_{rel_change}(home). The variables considered as predictors were an individual’s biting suitability score (which accounts for biting attractiveness, avoidance behavior, and defensive behavior), the number of mosquitoes in their home, the number mosquitoes in their activity space pre-exposure (places they frequented), and the percent of expected mosquito bites that occur at their home pre-exposure (i.e., prior to being bitten by a virus-infected mosquito). Best-fit was determined with ΔAICc and the percent of deviance explained by each model.

For the third objective, we calculated the expected number of mosquito contacts for each individual pre-exposure and at each stage of infectiousness (I₁ –I₅). Expected counts were calculated as row sums of B_norm and each B_i matrix. For all individuals that experienced infection, the change in number of expected mosquito contacts was calculated for each infectiousness stage, as compared to pre-exposure. Percent change was also calculated to account for variation in healthy mosquito contact counts

We examined the importance of these variations in healthy mosquito contacts for the entire population. Further, given the epidemiological significance of those with the top 20% of contacts [15], we also compared individuals with the top 20% of expected contacts pre-exposure to the rest of the population (bottom 80%). B matrices were also used to determine the percent of an individual’s mosquito contacts that occurred at their home. Generalized additive models (GAMs) were examined for change in expected mosquitoes contacts, both as a number and a percent. Predictors and methods for finding best-fit models are as mentioned above. All statistical analyses were performed in R 3.3.0 statistical computing software.

Epidemic dynamics

Compared to a baseline model, naïve to pre-symptomatic infectiousness and disease-driven mobility change, a model including both parameters predicted an increase of 37% in the probability of a DENV outbreak occurring (from 39.5% to 76.0%) (Table 3). Models only including disease-driven mobility change or pre-symptomatic infectiousness increased the probability of an outbreak by 14% and 15.5%, respectively, compared to the baseline model (Table 3). In the simulations where outbreaks did not occur, the infection only spread to a few people (<1% of the population) before virus transmission ceased. For simulations leading to epidemics, the inclusion of pre-symptomatic infectiousness had minimal effects on parameters such as time to peak infection, the length of the epidemic, or how many individuals were infected (Table 3). Adding symptomatic mobility reductions to the baseline scenario had little effect on when the epidemic peaked or how long it lasted; however, there was a 5.2% decrease in peak transmission (Table 3). This reduction was compensated by accounting for infectiousness in the pre-symptomatic period (Fig 2).

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Fig 2. Predicted influence of disease-driven mobility reduction and pre-symptomatic transmission on DENV epidemic dynamics.

For each scenario and for each time step, (main) the average proportion of infected hosts is calculated and (inset) the average proportion of cumulative infections is calculated. Averages are calculated across all simulation runs where an outbreak occurred, with standard deviations included in the shaded ribbons. The baseline scenario is one with no mobility change and no pre-symptomatic period.

https://doi.org/10.1371/journal.pcbi.1008627.g002

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Table 3. Dengue virus predicted infection prevalence based on presence of pre-symptomatic period and/or disease-driven mobility reductions, presented as mean ± 2SEM.

https://doi.org/10.1371/journal.pcbi.1008627.t003

Onward transmission

At the population level, the distributions of onward transmission (R) values increased slightly when disease-driven mobility reductions were added to a model without them (from an average ± SD of 5.4 ± 5.1 to 5.9 ± 4.8 infections) (Table 4 and S3 Fig). At the individual level, models including mobility change led to a reduction of the importance of out-of-home onward transmission by symptomatic individuals (reductions in R for primary and secondary bites of -62% and -17%, respectively) at the expense of an increase in the relevance of their home (increases in R for primary and secondary bites of 40% and 32%, respectively) (Table 4 and Fig 3). While the home environment played a key role in primary infections (Fig 3A), the majority of secondary infectious bites contributing to transmission occurred at other houses (Fig 3B).

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Fig 3. Expected onward transmission (R) values with and without mobility changes accounted for, separated by where primary bites occur and where secondary bites occur.

(A) gives onward transmission for primary bites occurring at home (red) and at other houses (blue) both without (left) and with (right) movement change included. [from left to right: R_norm(home), R_norm(other houses), R_movement(home), and R_movement(other houses)] (B) gives onward transmission for secondary bites at the home of the primary infected individual (red) and at other houses (blue).

https://doi.org/10.1371/journal.pcbi.1008627.g003

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Table 4. Average onward transmission (R) values with and without mobility change and change in R due to mobility change inclusion.

https://doi.org/10.1371/journal.pcbi.1008627.t004

GAMs fitted to individual-level estimates of R_movement allowed understanding the mechanism by which mobility change influences onward transmission. In univariate GAMs, biting suitability score (which accounts for biting attractiveness, avoidance behavior, and defensive behavior) and number of mosquitoes at home pre-exposure explained 32.3% and 27.7% of deviance, respectively, whereas percent of bites expected at home pre-exposure only explained 9.3% of deviance (S5 Table). Increases in onward transmission were best explained by a GAM including an individual’s biting suitability and the number of mosquitoes in their home as well as their interaction (S5 Table and S4 Fig). Increasing biting suitability score from 0 to 1 only increased predicted R_movement by 5 new infections when there was a low mosquito count at home (e.g., 1–3 mosquitoes), compared to an increase of 30 new infections for those with high mosquito density (e.g., 40–50 mosquitoes) at home (Fig 4). In univariate GAMs for the two main parameters, including mobility change (R_movement) increased dramatically the % deviance explained compared to models excluding it (R_norm) (from 13.6% to 27.7% for number of mosquitoes at home and from 37.7% to 67.1% for biting suitability score) (S4 and S5 Tables). Including a variable for the total number of mosquitoes in all houses an individual visited pre-exposure (their activity space) did not increase the fit of GAMs (S4 Table).

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Fig 4. Predicted contribution to onward DENV transmission (R_movement) based on a GAM with predictor variables number of mosquitoes in home pre-exposure, biting suitability score, and their interaction.

The predicted values of onward transmission based on biting suitability and number of mosquitoes at home pre-exposure, presented as a heatmap with contours.

https://doi.org/10.1371/journal.pcbi.1008627.g004

Disease-driven mobility changes can either increase or decrease an individual’s contribution to onward transmission. GAMs were fitted to the percent change in onward transmission arising from all mosquito bites (R_{rel_change}) and from only bites at home (R_{rel_change}(home)). For both metrics, the best-fit GAM included biting suitability, percent of bites at home, their non-linear interaction, and the number of mosquitoes at home (S6 Table). For R_{rel_change} there was only a 3.0% loss in deviance explained when instead using a univariate GAM with percent of bites expected to occur at home pre-exposure (from 83.6% to 80.6%) (S6 Table). When the percent of bites expected to occur at home pre-exposure was below 61%, there was a predicted decrease in onward transmission, whereas those with greater than 61% of bites expected at home pre-exposure saw increases in onward transmission when mobility was accounted for (Fig 5A). The notable exception to this monotonically increasing effect was the tempered increase in onward transmission for those who received their pre-exposure bites almost exclusively at home (Fig 5A). When examining the percent change in onward transmission only from primary bites at home (R_{rel_change}(home)), a majority of the deviance was explained in a model with percent bites expected at home pre-exposure and biting suitability score (S6 Table). The percent change in onward transmission from primary bites at home was predicted to be positive for all individuals, with the largest percent increase for individuals with low biting suitability scores and a small percent of bites expected to occur at home pre-exposure (Fig 5B).

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Fig 5. Predictions for percent change in expected onward transmission when mobility is included ((R_{rel_change} and R_{rel_change}(home)) based on GAM models.

(A)The predicted percent change in onward transmission (R_{rel_change}) based on percent of bites expected at home pre-exposure. (B) The predicted percent change in onward transmission from primary bites at home R_{rel_change}(home)) based on biting suitability and percent of bites expected at home pre-exposure, presented as a heatmap with contours.

https://doi.org/10.1371/journal.pcbi.1008627.g005

Human-mosquito contacts during illness

At the population level, the number of expected mosquito contacts was similarly distributed for each infectiousness sub-stage: before (I₁), during (I₂ –I₄), and after (I₅) mobility changes occurred (S7 Table and S5 Fig). When examining the change in an individual’s expected contacts at each infectiousness sub-stage during symptomatic mobility change, however, 57% of individuals had a decrease in expected contact and 38% had an increase (S6 and S7 Figs). During the first three days after symptom onset, the average change in expected contacts for individuals who received the top 20% and bottom 80% of expected mosquito contacts pre-exposure was a decrease of 13% and 17.5%, respectively (Table 5). Further, of those individuals who received the top 20% of expected mosquito contacts pre-exposure, 24% had a large enough decrease in mosquito contacts on the first three days after symptom onset to no longer be in the top 20% when symptomatic (S5 Fig).

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Table 5. Average change in expected mosquito bites for each infectiousness sub-stage when symptomatic mobility changes are occurring (I₂ –I₄), separated based on expected bite values pre-exposure, provided as raw number and percent change relative to expected bites pre-exposure.

https://doi.org/10.1371/journal.pcbi.1008627.t005

The percent change in expected mosquito contacts from pre-exposure to the first three days after symptom onset was best explained by a GAM including biting suitability score, percent of bites expected at home pre-exposure, number of mosquitoes at home pre-exposure, and the interaction between biting suitability and percent bites at home, which explained 93.4% of deviance (S8 Table). The model with only a term for percent of bites expected at home pre-exposure, however, was able to explain 92.1% of the deviance (S8 Table). The effect of percent bites at home on percent change in expected mosquito contacts was very similar to the effect on percent change in onward transmission, where those with less than 55% of bites at home pre-exposure had a predicted decrease in mosquito contacts during the first three days of illness and those with greater than 55% were predicted to see an increase in mosquito contacts (S8 Fig). Individuals who received none of their bites at home pre-exposure were expected to have the largest percent decrease, whereas those who received around 90% of their bites at home pre-exposure had the largest percent increase (S8 Fig). If change in expected mosquito contacts was examined as a raw value rather than a percent change, all three variables and the interaction between number of mosquitoes at home and percent of bites at home were needed to provide an accurate prediction and explain a large amount of the deviance (S8 Table).

Sensitivity analyses

For the scenario where only 30% of cases experienced symptoms (and symptomatic mobility change), the expected values and relative changes for onward transmission and human-mosquito contacts had similar dynamics as in the case above where all individuals were symptomatic (S9–S15 Tables and S9–S13 Figs). In the scenario where symptomatic individuals did not completely halt mobility the first three days of symptoms, we saw similar albeit less intense dynamics as those above (S16–S22 Tables and S14–S18 Figs). This relaxing of mobility rules on the first days of symptoms did, however, curtail the importance of the household in onward transmission. When mobility was halted on days 1–3 there was a 40% increase in expected onward transmission from primary bites at home, as compared to a 30% increase with partial mobility on days 1–3 of symptoms (Tables 3 and S16 and S18 Fig). Similarly, the decrease in expected onward transmission from bites at other residential locations was 62% with no mobility on days 1–3 and 48% with partial mobility (Tables 3 and S16 and S18 Fig). Given this decreased role of the household in the case of partial mobility on the first three days of symptoms, the best-fit GAMs for predicting changes in onward transmission and changes in mosquito contacts could explain less deviance than their counterparts above (S17–S19 and S22 Tables). Regardless, we still saw the same predictor variables/reduced models with the largest contribution to explaining outcomes in the cases with partial and no mobility during days 1–3 after symptom onset (S17–S19 and S22 Tables).