Space-time personalized short message service (SMS) for infectious disease control – Policies for precise public healthPlease cite this article as: Ling Yin, Applied Geography, https://doi.org/10.1016/j.apgeog.2019.102103
0143-6228/© 2019 Elsevier Ltd. All rights reserved.
Space-time personalized short message service (SMS) for infectious disease control – Policies for precise public health
Ling Yin a, Nan Lin a, Xiaoqing Song a,b, Shujiang Mei c, Shih-Lung Shaw d, Zhixiang Fang e, Qinglan Li a, Ye Li a, Liang Mao f,*
a Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China b College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, Anhui, China c Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China d Department of Geography, University of Tennessee, Knoxville, TN, USA e State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China f Department of Geography, University of Florida, Gainesville, FL, USA
A R T I C L E I N F O
Keywords: Short message service (SMS) Dengue fever Mobile phone tracking Trajectory analysis Precision health
A B S T R A C T
Mobile phones and short message service (SMS) have been widely used in disease control and prevention. Personalized SMSs further allows real-time, precisely targeted interventions that achieve better cost- effectiveness. Few SMSs are personalized based on spatiotemporal travel behavior of individuals, which plays an important role in disease spread. We proposed a set of SMS policies tailored to individuals’ travel behavior derived from massive mobile phone tracking records. These policies tend to alter spatial, temporal, or spatio- temporal patterns of individuals’ daily activities, in order to reduce the risk of disease spread. Taking Shenzhen city, China, as a study area, we simulated and evaluated these policies for Dengue Fever intervention. Our simulation results show that the spatially targeting policy that discourages discretionary trips produces the highest cost-effectiveness to control disease spread in areas with high importation risk. For the entire city, however, the temporally targeting policy that shifts individuals’ travel schedules achieves the best cost- effectiveness. Our study contributes to a new ground of precise public health that calls for individualized, real-time, and accurately targeted interventions. Utilizing big mobile phone data, we present a novel approach to design, simulate, and evaluate space-time precise intervention for disease control.
1. Introduction
Mobile phones and short message service (SMS) that are already a part of people’s daily life are powerful tools to improve health by alerting disease risk and assisting disease prevention (Badawy & Kuhns, 2017; Fjeldsoe, Marshall, & Miller, 2009; Krishna, Boren, & Balas, 2009). For instance, short messages were sent to a large number of subscribers with health promotion slogans for HIV/AIDS in India, to encourage parents to get their children vaccinated in Pakistan, to pro- mote hand-washing in Nepal for preventing diarrhea, and to remind the population about bed net distribution for malaria control in Africa (Deglise, Suggs, & Odermatt, 2012).
In addition to the ‘one-message-fits-all’ policy, the SMS can be further personalized to improve intervention effectiveness. For in- stances, Franklin, Waller, Pagliari, and Greene (2006) reported an
increased adherence to medicine taking when sending patients daily text-messages with personalized goal-specific prompts and tailored to patient’s age, gender, and insulin regimen. Yoon and Kim (2008) found that weekly optimal advice via SMS based on patients’ medication detail, diet and exercise can rapidly improve and stably maintain their health measures. Therefore, the personalized SMS is considered as a promising policy in ‘precision public health’, which calls for ‘providing the right intervention to the right population at the right time’ to maximize cost-effectiveness (Khoury, Iademarco, & Riley, 2016; Patrick, Griswold, Raab, & Intille, 2008).
Although SMSs can be tailored by a variety of personal information for disease control, little attention has been paid to individual’s travel pattern over space and time. The important role of travel behavior or human mobility in spreading diseases has been well documented, particularly for communicable diseases, such as flu and dengue fever (Le
* Corresponding author. 3141 Turlington Hall, Gainesville, FL, 32601, USA. E-mail address: liangmao@ufl.edu (L. Mao).
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Menach et al., 2011; Meloni et al., 2011; Huang, Das, Qiu, & Tatem, 2012; Wesolowski et al., 2012). Travel restriction has also been widely considered as an effective intervention policy for epidemic control (Bajardi et al., 2011; Germann, Kadau, Longini, & Macken, 2006). The personalized SMSs based on travel behavior, however, were not well studied in the literature because of two research challenges: how to obtain detailed individual travel records for a massive population; and if succeed, how to tailor the sent message for each individual based on this ‘big’ data.
Mobile phone tracking technology has been recognized as one of promising solutions toward ‘precision’, due to its popular use, timeli- ness, and location awareness (Patrick et al., 2008; Wesolowski, Buckee, Engø-Monsen, & Metcalf, 2016; Lai, Farnham, Ruktanonchai, & Tatem, 2019). Millions of mobile phone tracking records make it possible to depict movements and contacts of individuals in unprecedented details (Blondel, Decuyper, & Krings, 2015; Deville et al. 2014; Gonzalez, Hi- dalgo, & Barabasi, 2008; Schneider, Belik, Couronne, Smoreda, & Gonzalez, 2013; Song, Qu, Blumm, & Barabasi, 2010; Xu et al. 2016). When combined with disease incidence or prevalence, the mobile phone data can be used to predict movements of disease carriers, and identify sources and sinks of disease dispersion, then suggesting targeted control and elimination programs to the right places (Bengtsson et al. 2015; Finger et al. 2016; Mao, Yin, Song, & Mei, 2016; Meankaew et al. 2010; Le Menach et al., 2011; Panigutti, Tizzoni, Bajardi, Smoreda, & Colizza, 2017; Peak et al., 2018; Tatem et al. 2014; Wesolowski 2015; Weso- lowski et al. 2012, 2014). In the current literature, however, few of these intervention programs could be labelled ‘space-time personalized’, even though big data were heavily utilized. First of all, few of these inter- vention programs are personalized at a fine temporal scale. A majority of studies have been focused on spatial risk mapping and intervention, for instance the recent work in Namibia (Tatem et al. 2014), Kenya (Wesolowski et al. 2012), and Zanzibar region of Tanzania (Le Menach et al., 2011). The suggested intervention strategies were spatially ori- ented but did not consider temporal dynamics of individuals’ travel behavior. Wesolowski et al. (2017) only used mobile phone tracking data to explore seasonal travel and then inter-seasonal intervention strategies within a year. Despite high temporal resolution (e.g., hourly) offered by mobile phone tracking technology, current studies have seldom taken this advantage to investigate temporally resolved in- terventions, such as the best hours in a day to deploy intervention. Second, many studies are not spatially personalized either (Lee et al. 2016). Since most of these studies were oriented to entire countries, the smallest spatial unit for analysis and disease intervention is often large in size. For example, the study in Namibia considered 402 basis units for analysis (Tatem et al. 2014), which makes each spatial unit covering 2000 km2 wide on average. Interventions targeted to such large spatial units could be costlier in time and efforts than those to small areas or to specific individuals. In a nutshell, the current literature has not made full use of the high spatiotemporal resolution offered by the mobile phone big data. A major reason is these studies were conducted at a national level, and the detailed mobile phone records needed to be aggregated to coarser spatial and temporal scales (e.g., by prefecture and by season) to ease the analysis. To the best of our knowledge, there are few attempts to explore spatiotemporally personalized interventions for disease outbreaks.
To fill the research gap, we proposed a set of SMS policies tailored to individuals’ travel behavior with a high spatiotemporal resolution, which were derived from massive mobile phone tracking records. Our proposed policies tend to alter spatial, temporal, or spatiotemporal patterns of individuals’ daily trajectories, in order to contain the spread of diseases. Taking Shenzhen city, China, as a study area, we simulated and evaluated these policies for controlling dengue fever outbreak.
In 2014, the Guangdong province in South China was swept by an unexpected dengue fever epidemic, which started with 423 cases in August and reached 42,358 cases by the end of October (Cheng et al., 2016; X.; Jin, Lee, & Shu, 2015; Lin et al., 2016). Aedes albopictus is the
major vector of Dengue fever in this study area (L. Q. Jin & Li, 2008). As the second largest city in the province (Fig. 1), the study area had also experienced an abrupt increase of dengue infections in 2014 and is now scaling up its dengue control program to fight future outbreaks of dengue and Zika fever.
2. Materials and methods
2.1. Mobile phone tracking data
The mobile phone dataset is produced by the China Mobile Tele- communications Company, and obtained from Shenzhen Transportation Operation Command Center for research purposes. It tracks millions of anonymized mobile phone users during a regular weekday (24 h in total) in 2012 without any major event. The mobile phone tracking dataset is generated from real-time monitoring of phone signals through a network of cell towers. Each cell tower gives a cellular coverage to an area that is often approximated by a non-overlapping Thiessen polygon, hereinafter referred to as the phone catchment area. The radius of a catchment area varies from 200 m to 2 km, dependent on the proximity between towers. A cellphone is connected to a cell tower when entering the tower coverage area, and meanwhile obtains a current location as latitude and longitude of the cell tower. It is noteworthy that this dataset is different from the caller detail records (CDR) widely used in many other litera- ture, in that the mobile phone status, including its time and location, is recorded at a regular basis, i.e., every 0.5–1 h, no matter whether a telecommunication transaction is made or not.
The China Mobile Telecommunications Company takes around 75% of mobile phone user market in Shenzhen, and the original mobile phone tracking dataset includes 16 million anonymous mobile phone users. Due to signal loss and power off issues, a portion of mobile users were not tracked and lost their records during some hours, leading to gaps in location tracking. We only selected users who had at least one record in every hour of the 24 h to guarantee continuous tracking. Since this se- lection procedure may introduce sampling bias over space, we employed a random resampling strategy to mitigate such bias. Specifically, we first identified the most frequently appeared location during the nighttime (0:00–6:00) as an estimated home location for each mobile phone user, from which we calculated the user distribution as percentages over 10 administrative regions of Shenzhen city (Fig. 1). Following the user distribution, we randomly sampled a proportion of mobile phone users who had continuous tracking records from each of 10 administrative regions, which finally formed a dataset of 3.87 million mobile phone users. For each mobile phone user, the travel trajectory is composed of 24 hourly records, and each record has three elements: an anonymous user ID, the latitude and longitude of currently connected cell tower, and the time period of recording. Each time period spans for an hour, i.e., 0:00–1:00, 1:00–2:00, …, 23:00–24:00, respectively. Since the mobile phone records are updated every 0.5–1 h, for users who happen to have more than one location records within a time period, we only kept the first recorded location.
2.2. Baseline scenario: local and importation risk of dengue fever
To form a baseline scenario of no SMS interventions, two types of dengue transmission risk were estimated, namely the risk of local infection and that of importation through individual travels. The local infection risk of an area indicates the likelihood of acquiring dengue fever virus from existing cases within the same area. Since dengue fever is vectored by mosquitoes, the local risk map also implies spatial risk of local mosquito bites that cause dengue infection. On the other hand, the importation risk of an area measures the repeated introduction of dengue fever into the area, via human mobility, that can initiate or sustain the transmission there. Detailed procedures for estimating both risks are elaborated in the previous study by Mao et al. (2016), and are briefly described below.
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To map the local infection risk, de-identified data for dengue fever cases from February 2013 to December 2014 were collected by the Shenzhen Disease Prevention and Control Center. A total of 489 confirmed cases were georeferenced onto the street map as points using their reported home addresses. Among these dengue fever cases, there were 350 local infection cases and 139 imported cases based on their travel history records. Spatial covariate datasets were assembled to represent weather (the minimum, maximum and mean of temperature and rainfall from June to December), topography, land cover, land use, and population at 100 m resolution. A scalable grid resolution of 100 m was chosen so that each phone catchment area (with a radius from 200 m to 2 km) can have at least four grid cells and thus the heteroge- neity of infection risk within it can be represented. A 100-m local risk grid map was then created following the random forest classification procedures outlined by Cohen et al. (2013). Note that the resulting local risk map did not vary over time during the epidemic. Instead, this research focused on spatial heterogeneity of infection risk, meanwhile simplifying the temporal dynamics as an average day of the epidemic.
By combining the local risk map and the mobile phone tracking dataset, the importation risk into a phone catchment area was further calculated as a sum of infection risk of every visitor, formulated as Equation (1):
IRL¼ XNL
i¼1 Pri (1)
Where Pri ¼ 1 Q24
t¼1ð1 RLðtÞÞ AðtÞ IRL denotes the importation risk to a
phone catchment area L, i.e., the number of imported infections per day. i represents an individual and NL is the total number of individuals who visited L during a day. Pri is the total probability of acquiring dengue fever virus for individual i over 24 h in a day. RL(t) is the local infection risk at a phone catchment L derived from the local risk map. t (¼1, 2 …, 24 h) indicates the time of visit in hour and the exponent A(t) is the standardized activity level of mosquitoes in blood meal hunting during time t derived from the observational study (Li, Li, & He, 2004) (Fig. 2). Details can be found in the supplementary file and a published work (citation blinded for review).
Using Eq. (1), we identified top 50 phone catchment areas with remarkably high importation risk (Fig. 3), hereinafter referred to as high-importation-risk areas (HIR_Areas). A total of 310,000 individuals who visited HIR_Areas were referred to as high-importation-risk visitors
(HIR_Visitors), including those who stay at this area for certain time or just travel through this area. These HIR_Areas and HIR_Visitors were set as a baseline scenario for exploring intervention polices.
2.3. Space-time personalized SMS policies
We proposed a set of travel intervention policies that precisely target HIR_Visitors with particular spatial, temporal, or spatiotemporal travel patterns by sending them text messages regarding recommendations against dengue infection. We assumed these messages could alter their daily travel behavior over space and time, and consequently reduced the importation risk.
1) Spatially targeting policies (Sp) aim to reduce the number of HIR_Visitors by changing their travel destinations, but not the time sequence. In other words, a text message is sent to all HIR_Visitors and suggests them to avoid visiting a list of HIR_Areas. We designed two types of messages: the first type (S1p) says “Due to dengue fever outbreak recently, please consider cancelling all your trips today to following HIR_- Areas: XXX, …“; the second type of messages (S2p) states, “Please cancel your discretionary trips to following HIR_Areas: XXX …“, aiming to discourage non-obligatory trips destined to HIR_Areas. Compared to S1p,
Fig. 1. Geographic location of Shenzhen city in Guangdong province, China and its administrative districts.
Fig. 2. Hourly activity levels of Aedes albopictus and human movements during a day in the study area.
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the policy S2p is less restrictive on travel behavior, because it does not affect obligatory trips (e.g., work trips) to HIR_Areas.
As Fig. 4 illustrates, to simulate S1p, we identified trips with desti- nations at HIR-Areas and trips passing by HIR-Areas from trajectories of HIR-Visitors. First, for trips with destinations at HIR-Areas, we further divided them into two types. One type was commuting trips such as going to work and school, and the other type referred to non-commuting trips such as shopping and entertainment. For commuting trips with destinations in HIR-Areas, we reset their destinations from workplaces and schools back to homes. This is because, for each mobile phone user, we did not have information regarding alternatives for these must-visit places. For the non-commuting trips with destinations in HIR-Areas, we re-routed their destinations to a randomly selected phone catchment area not in HIR-Areas but with a similar travel distance. Here the travel distance was measured as the Euclidean distance between a mobile phone user’s departure cell tower and arrival cell tower. Second, for a trip passing by HIR-Areas, regardless of its trip purpose, commuting or non-commuting, we created a detour route to replace its original tra- jectory. Specifically, we first identified a passing point Pk locating at HIR-Areas from a trip trajectory. Then we replaced Pk with its nearest cell tower outside HIR-Areas. This step was repeated until all passing point Pk in HIR-Areas were replaced.
To simulate S2p, we identified non-commuting trips with destina- tions at HIR-Areas and trips passing by HIR-Areas from trajectories of HIR-Visitors. Using the same simulation as S1p, we reset the destinations of non-commuting trips and create detour routes for trips passing by
HIR-Areas. 2) Temporally targeting policy (Tp) does not reroute HIR_Visitors to
other destinations but aims to reduce their stay time in HIR_Areas by shifting departure/arrival time of their trips. According to hourly ac- tivities of Aedes albopictus in the study area, 5:00–8:00 and 18:00–20:00 are two peak periods of blood meal hunting (Li et al., 2004), overlapping with the two peak periods of human travels (7:00–10:00 and 17:00–20:00) (Fig. 2). To reduce such overlap, the Tp policy recom- mends mobile phone users to adjust their travel time to avoid staying at areas with higher risk during peak hours of mosquito biting. For simu- lation, we examined each HIR_Visitor’s temporal pattern of traveling, and designed a personalized message, such as “Please adjust your departure/arrival time of next trip to XX: XX to avoid mosquito biting peak time”.
Specifically, as Fig. 5 illustrates, Home_RL denoted the local infection risk RL of a user’s home location. FirstStay_RL was referred to as the local infection risk RL of the first destination after leaving home in the morning (5:00–10:00). LastStay_RL is the local infection risk RL of the last destination before returning home in the evening (17:00–23:00). The simulation of Tp strategy for a mobile phone user is described as the follows.
To adjust the departure time from home in the morning: a) If First- Stay_RL Home_RL and the user left home before 8:00, then we sent a message as “Please leave home after 8:00”. We simulated this strategy by postponing the user’s departure time to 8:00. b) If First- Stay_RL <Home_RL and the user left home after 6:00, then we sent a
Fig. 3. The identified high-importation-risk areas and high-importation-risk visitors to control Dengue fever in Shenzhen city. The visitors of high-importation-risk areas mainly come from neighboring areas.
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Fig. 4. Flowchart of simulating the effect of spatial targeting policies.
Fig. 5. Simulating the effect of temporal targeting policy.
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message as “Please leave home before 6:00”. We simulated this strategy by moving up the user’s departure time to 6:00. Note that although the morning peak of mosquitos’ activity level starts from 5:00, it was not usual for regular working people to leave home before 5:00, and therefore, we chose 6:00 as the start of the intervention policy.
To adjust the arrival time at home in the evening: a) If Last- Stay_RL Home_RL and the user return home after 18:00, then we sent a message as “Please return home before 18:00”. This was simulated by moving up the user’s arrival time to 18:00. b) If LastStay_RL <Home_RL and the user returned home before 20:00, then we sent a message as “Please return home after 20:00”. This was simulated by postponing the user’s arrival time to 20:00. Note that users might leave home again at night, but these night-outgoing trips were beyond our intervention.
Both of the above messages for departure time in the morning and arrival time in the evening were sent to users if their travel pattern met the criteria.
3) Spatiotemporally targeting policies (STp) aim to reduce total number of trips of HIR_Visitors through cancelling trips for a certain time period, which changes both spatial and temporal sequence of a trajec- tory. We designed two types of messages: the first type (ST1p) stated “Please stay at home from 18:00 to 8:00 to reduce the risk of dengue infection”, aiming to discourage all trips during peak hours of mosquito bites. We simulated this policy by anchoring HIR_Vistors’ trajectories at home between 18:00 and 8:00. The second type (ST2p) of messages is an extreme travel restriction, stating that “Please stay at home all day today”. We simulated this policy by anchoring the whole trajectory of a HIR_- Vistor at home.
To further consider people’s compliance to SMS, we examined ten compliance levels of mobile phone users to each intervention policy, from 0% to 100% with an increment of 10%. For each compliance level we randomly select a fraction of HIR_Vistors to form a compliant population.
2.4. Evaluation of intervention effectiveness and cost-effectiveness
2.4.1. Local and regional effectiveness To evaluate the intervention effectiveness in each phone catchment
area, we computed a local reduction ratio of importation risk (RR_IRL), formulated as Equation (2):
RR IRL ¼ IR’
L IRL
IRL (2)
where IRL and IR’ L denote the importation risk to a phone catchment area
L before (the baseline scenario) and after an intervention, respectively. This local measure was further aggregated to a regional level as an averaged reduction ratio (ARR_IR) in Equation (3) and a total reduction of importation risk (TR_IR) in Equation (4):
ARR IR¼ 1 M XM
L¼1 RR IRL (3)
TR IR¼ XM
(4)
where M is the total number of phone catchment areas in a region. The region can be either the HIR_Areas or the entire city.
2.4.2. Cost-effectiveness analysis To further evaluate the feasibility, we investigated the trade-off be-
tween the effectiveness and costs of each intervention policy. The costs, here, refer to negative impacts on people’s daily travels and activities, due to cancelling trips and being grounded at homes. For each inter- vention policy, the resulting costs were gauged by the Total Trips Cancelled and the Total Increased Hours at Home for all HIR_Visitors. The cost-effectiveness was then measured as ratios of effectiveness over
costs, i.e., TR_IR per Trip Cancelled and TR_IR per Increased Hour at Home. To account for randomness, we simulated each intervention policy
100 times at each compliance level, and calculated the mean value, as well as the coefficient of variance (CV), for those effectiveness and cost- effectiveness indicators.
3. Results and discussion
3.1. SMS intervention effectiveness for HIR_Areas only
We first focused our analysis on HIR_Areas, the top 50 phone catch- ment areas with the highest importation risk. The effectiveness of all policies is linearly proportional to the compliance level (Fig. 6A). This is because the importation risk was calculated as an arithmetic sum of infection risk of every visitor in Equation (1). The maximum variability was limited to 1.33% around the mean value (Fig. 6B). indicating that the simulated effectiveness was well stabilized after 100 realizations. The stability of simulation results can be attributed to the similarity and regularity of local residents’ movements. That is, the HIR_Visitors mainly moved locally (Fig. 3), which increased the similarity of their visited places. Previous studies have also revealed that most of urban residents have less than four frequent activity locations and often have similar movement patterns among their frequent activity locations (Schneider et al., 2013; Yin et al., 2015).
The most effective SMS policy was the spatiotemporally targeting policy ST2p that urged HIR_Visitors to stay at home all day in a day. It achieved the highest ARR_IR in HIR_Areas across all compliance levels, given that the spread of dengue fever was highly localized. The policy that follows was the spatially targeting policy S1p, which asked HIR_- Visitors to avoid all trips to HIR_Areas. This policy was effective because a majority of HIR_Visitors lived around HIR_Areas (Fig. 3), and most of their trips were related to HIR_Areas, either destined to or through HIR_Areas. For the same reason, it was not surprising that another spatially targeting policy (S2p) ranked the third in effectiveness, since it suggested HIR_Visitors to only avoid discretionary trips to HIR_Areas.
The fourth effective policy was the spatio-temporally targeting pol- icy (ST1p) that recommended targeted users staying at home from 18:00 to 8:00. This intervention policy reduced trips of HIR_Visitors during the peak hours of mosquito activity, and resulted in two offsetting processes regarding importation risk. On one hand, staying at home decreased the number of visits to HIR_Areas, and lowered the importation risk to HIR_Areas. On the other hand, if the local infection risk at home was higher than the places they were supposed to go, staying at home during peak hours would otherwise increase the chance of acquiring dengue fever, which in turn raised the importation risk when they traveled in non-peak hours. The simulated effectiveness of ST1p arose from these two offsetting processes and hence was not as effective as the previous three policies. Lastly, the temporally targeting policy (Tp) was the least effective one for HIR_Areas. It only shifted HIR_Visitors’ travel time, not their destinations. The control effect was not as direct as other four policies that reduced visitor volume into HIR_Areas.
3.2. SMS intervention effectiveness for entire city
Beyond HIR_Areas, SMS intervention policies also affected other areas of the entire city. Heat maps in Fig. 7 show the estimated RR_IR for each mobile phone catchment area given an 80% compliance level. The proposed policies produced positive effects at some areas (RR_IR > 0 as warm colored), but also negative effects at some other places (RR_IR < 0 as cold colored).
With regard to the spatially targeting policies (Fig. 7A and B), posi- tive effect areas were primarily HIR_Areas, where mobile users were suggested not entering. Around these positive areas, there were negative effect areas as results of trip substitution and detour. For trip substitu- tion, HIR_Visitors, possibly with infection, were simulated to visit other alternative places with similar travel distance instead, thus affecting
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Fig. 6. The intervention effectiveness for HIR_Areas and the variability of intervention simulation estimates at different compliance levels. (A) The simulated average reduction ratios of importation risk (ARR_IR) for HIR_Areas. (B) The coefficient of variance (CV) of simulation estimates.
Fig. 7. Spatial distribution of the reduction ratios of importation risk (RR_IR) for the entire study area by: (A) the spatial targeting policy (S1p); (B) the spatial targeting policy (S2p); (C) the temporal targeting policy (Tp); (D) the spatiotemporal targeting policy (ST1p); (E) the spatiotemporal targeting policy (ST2p) with compliance level of 80%. Warm colors indicate positive effect (reduced importation risk) and cold colors indicate negative effect (increased importation risk). Simulation results at other compliance levels can be found in the supplementary file. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
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other areas negatively, especially neighboring areas due to the travel distance constraint. For detour, HIR_Visitors who should have passed by HIR_Areas were simulated to choose nearby detour routes, thus importing infection risk into neighbor areas.
The temporally targeting policy (Fig. 7C) generated relatively small change of importation risk for the entire city. In other words, it did not reduce importation risk as effectively as the spatially targeting policies, but it did not negatively affect other places either. The spatiotemporally targeting policies (Fig. 7D and E) produced the largest positive effect areas among all proposed policies. Their positive effect centered at the HIR_Areas and radiated outward. Since individuals were limited at home, this policy had no severe negative effect for other places.
Fig. 8 compares the overall performance of policies for the entire city and for the HIR_Areas only. The total importation risk reduction (TR_IR) of spatially targeting policies (S1p and S2p) for the entire city was even less than that for HIR_Areas. Therefore, the spatially targeting policies had strong local effectiveness, but were not optimal for global disease control. On the other hand, the temporally targeting policy produced significantly higher total risk reduction for the entire city than that for the HIR_Areas. For city-wide intervention, the temporally targeting policy outperformed the spatially targeting policies from a perspective of global optimization. Furthermore, the spatiotemporally targeting policies remain the most effective for the entire city.
3.3. Cost-effectiveness analysis
Feasible policies are often trade-off between effectiveness and costs. Given the compliance level of 30% and 80% as examples, Table 1 lists the indicators representing effectiveness, costs, and cost-effectiveness, respectively. For the HIR_Areas only, the spatially targeting policy (S2p) did not produce the highest effectiveness, but had the least costs, thus offering the best cost-effectiveness for HIR_Areas. This policy can reduce the total importation risk by 9.52 for every trip cancelled, and by 2.76 for every increased hour at home.
With regard to the entire city, the temporally targeting policy (Tp) that only adjusted people’s travel time produced the highest cost- effectiveness. It reduced the total importation risk by 4.43 for every trip cancelled, and by 1.65 for every increased hour at home. Thus, the temporally targeting policy can be considered as the optimal choice for dengue intervention over the entire city.
4. Discussions
4.1. The issues of data bias
The issue of data bias always comes with studies using mobile phone data (Lai et al., 2019). There are two major types of data bias in mobile phone data related to this study. First, mobile phone data only come from one mobile phone carrier company, which may not represent all mobile phone users. Second, even if the user market is large enough to represent all mobile phone users, mobile phone users may not represent all population, since children, elderly and socioeconomically disad- vantaged groups are likely to be underrepresented.
As for the first type of bias, the selected mobile phone carrier com- pany dominates the market in our study area with 75% share of total mobile phone users. For this reason, we believe our data from this company can well represented mobile phone users’ travel behavior, and well support our exploration on personalized intervention policies.
We are fully aware of the second type of bias in the mobile phone data. Children often live with and are supervised by adult family members. When adult family members receive intervention messages, they will guide their children’s travel behavior as instructed. For elderly and socioeconomically disadvantaged groups who do not use mobile phones, many of them are highly vulnerable to dengue, and should be informed about intervention via traditional offline strategies, for example, wall-posting of intervention information at where those people are most likely to gather.
4.2. Generalizability of the approach
Besides of dengue, the proposed approach can be extended to con- trolling other mosquito-borne diseases, such as malaria and Zika, in an urban context. There are three reasons for its generalizability. First, the estimation model for local infection risk includes spatial covariates representing weather, topography, land cover and land use, which are commonly related to many mosquito vectored diseases. Second, the design of temporally and spatiotemporally targeted policies depends on peak hours of Aedes albopictus, but can be easily substituted by other mosquito species. Third, the concept of importation risk and personal- ized intervention rely on a key assumption that human movements play a critical role in disease spreading, which is true for many mosquito- borne diseases.
Moreover, a main innovation of this study lies in the space-time personalized service based on individual’s daily travel pattern. This idea can be applied to broader infectious diseases that are spatially spread by human movements such as diseases by human-to-human transmission. In that case, the disease risk models and intervention models need to be tailored to the specific type of diseases.
4.3. Limitations of the approach
The estimation models of dengue transmission risk used in this approach cannot represent the dynamic process of a disease spread in both spatial and temporal dimensions. Therefore, it is difficult to inte- grate space-time precise intervention policies with the different stages of an epidemic. This can be solved by developing spatially explicit agent- based models to simulate disease dynamics (Fras-Mart;nez et al., 2011; Bian et al., 2012; Merler et al., 2015). In such a way, the power of mobile phone data to capture human spatiotemporal activities can be utilized to design staged space-time precise intervention. In the future studies, interactions among mobile phone users need to be considered, for example social contagion of their travel behavior and their compli- ance with interventions. We simply assumed that mobile phone users behaved independently, and thus the importation risk could be esti- mated as a linear sum of individual infection risks. A more realistic but non-linear scenario warrant a further investigation. Second, due to the data accessibility issue, we only used one day of mobile phone tracking
Fig. 8. The total importation risk reduction (TR_IR) of space-time intervention polices with compliance level of 80%.
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data to represent individuals’ daily activity-travel patterns. A mobile phone tracking dataset with a longer time span such as one week and one month, can offer more details in individual’s travel pattern. Besides, if longer periods of trajectory data are available, the prediction model of travel behavior can be developed along with external factors such as weather, holidays, major events and so on. The future importation risk can be estimated with the proposed model. Then, based on the predicted high-importation risk areas, future targeted people can be identified to tailor their space-time interventions accordingly. It would be more helpful if the personalized travel interventions are proposed based on the projected future travel behavior. Third, this study only focused on precise intervention regrading individual travel behavior. Other types of intervention such as vector control are not involved. In the future study, the combined intervention can be further explored for precise health.
5. Conclusions
Precision public health is promising, but more work lies ahead to develop a robust scientific foundation for use. Using dengue fever as an example, this study proposed a new approach that utilizes high- resolution massive mobile phone tracking data to design, simulate, and evaluate space-time precise intervention for disease control. This study reveals that each space-time personalized SMS policy has its own strengths and weakness. An appropriate policy choice depends on a comprehensive evaluation based on multiple factors including the focused area (high importation risk area or entire city), disease control effectiveness, and resulting costs. Particularly, to be cost-effective in practice, our simulation results show that the spatially targeting SMS policy to discourage discretionary travels could be a wise choice for controlling diseases within the high importation risk areas, while the temporally targeting SMS policy of shifting travel time is a cost-effective intervention policy for the entire city.
Funding sources
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi. org/10.1016/j.apgeog.2019.102103.
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L. Yin et al.
1 Introduction
2.2 Baseline scenario: local and importation risk of dengue fever
2.3 Space-time personalized SMS policies
2.4 Evaluation of intervention effectiveness and cost-effectiveness
2.4.1 Local and regional effectiveness
2.4.2 Cost-effectiveness analysis
3.3 Cost-effectiveness analysis
4.2 Generalizability of the approach
4.3 Limitations of the approach
5 Conclusions
Funding sources