Trends of Canine Rabies Lyssavirus and Impact of the ...philjournalsci.dost.gov.ph/images/pdf/pjs_pdf/vol... · barangay (barangay is a Filipino native term for village and is the

Keywords: castration, count regression model, IEC sessions, impounding, rabies, vaccination

*Corresponding Author: [email protected]

Trends of Canine Rabies Lyssavirus and Impact of the Intensified Rabies Control Program

in Davao City, Philippines: 2006–2017

Philippine Journal of Science148 (4): 751-763, December 2019ISSN 0031 - 7683Date Received: 16 Jul 2019

Zython Paul T. Lachica1, Sherelyn A. Evangelio1, Eliezer O. Diamante1, Abigail J. Clemente1, Johanna Marie Peralta1, Lyre Anni E. Murao2,

May Anne E. Mata1*, and Pedro A. Alviola IV3

1Department of Mathematics, Physics, and Computer Science2Department of Biological Sciences and Environmental Studies

3School of ManagementUniversity of the Philippines Mindanao, Davao City 8000 Philippines

Rabies remains a public health concern in the Philippines despite the national campaign against this viral disease. For instance, in Davao City – the largest city in the Philippines – rabies is endemic despite the implementation of Intensified Rabies Control Program (IRCP), which is comprised of mass dog vaccination, dog castration, dog impounding, and information campaigns. In this study, we report the trends of rabies in Davao City from 2006–2017, analyze the monthly pattern of rabies incidence in the city before and after the implementation of IRCP, assess the impact of IRCP using count regression models, and provide recommendations for the strategic implementation of the programs. Our result suggests that dog impounding, precipitation, and the presence of responsibly owned dogs are significantly associated with decreasing number of reported rabies cases. Thus, we recommend that, during summer seasons, the city’s impounding efforts must be intensified. Since dog impounding is resource-demanding and may cause instability in the dog population, we suggest that the city should push for the 70% minimum vaccination coverage. Finally, implementation of other interventions such as castration and information, education, and communication (IEC) sessions must be carefully assessed as well.

INTRODUCTIONRabies, a viral disease that is primarily transmitted through dog bites, results in approximately 59,000 estimated human rabies deaths annually in Africa and Asia (Hampson et al. 2015). Although the disease is 100% fatal, it is also 100% preventable through vaccination in humans and animals, which is the main intervention strategy for canine rabies elimination (Lavan et al. 2017, WHO 2015). Various collaborators such as United Against

Rabies have been formed with the common goal of zero human dog-mediated rabies deaths by 2030 worldwide (WHO 2018). In the Philippines, the government passed the Anti-Rabies Act of 2007 (R.A. 9482) (PAWS 2013) mandating the local government units to ensure that all dogs are properly vaccinated and registered. The R.A. 9482, together with dog impounding and castration, and conducting (IEC) sessions were implemented to achieve the goal of rabies-free Philippines by 2020 (DOH 2012). Moreover, the government has expanded its network of animal bite treatment centers to allow easier access to treatment for bite victims (Amparo et al. 2018a). These

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efforts resulted in 49 municipalities being declared by the Department of Health (DOH) as rabies-free since 2017, including Bohol (Junio 2017) and Camiguin island (Paculba 2018). However, rabies remains endemic in the country over the last decade (Amparo et al. 2018b).

Davao City is a highly urbanized city in the Philippines with a land area of 2,444 km2 and a human population of 1.633 million (PSA 2015). The city’s climate is tropical there is significant rainfall throughout the year where the average annual temperature is 27 °C and the average precipitation is 1,878 mm (NEDA- XI n/d). Presently, it has an estimated dog population of approximately 163,000 with an increasing number of rabies cases in the last four years: 12 cases in 2015, 15 cases in 2016, 21 cases in 2017, and 51 cases in 2018, according to the narrative reports of the Davao City Veterinarian’s Office (CVO). In response to Anti-Rabies Act of 2007, CVO has implemented four intensified rabies control strategies since 2011 such as mass vaccination of dogs, impounding, castration, and conduct of IEC sessions. Davao City has been implementing free canine rabies vaccination and castration of male dogs but was only intensified in 2011. This is performed either in a central station such as the barangay (barangay is a Filipino native term for village and is the smallest legislative unit in the Philippines) center or via house-to-house station. Dog impounding was launched in 2012 wherein free-roaming dogs are caught and taken to the city pound. IEC sessions have been conducted in coordination with barangay leaders to promote rabies awareness. Despite the implementation of these interventions, the number of reported rabies cases in the city continues to fluctuate, posing challenges to the rabies-free goal.

Identifying the drivers of the monthly reported rabies cases is necessary and measuring their effects is needed to improve the existing control strategies. These drivers include government-initiated interventions such as mass dog vaccination, dog impounding, dog castration, and conducting IEC sessions, as well as other drivers such as the dog characteristics and the weather conditions, which are known to affect rabies transmission and incidence. For example, the diurnal temperature range is significantly associated with positive rabies infection among submitted cases of striped skunks i.e., the enzootic risk to rabies of striped skunks increases with high diurnal temperature (Raghavan et al. 2016).

This paper presents the trends in the reported cases of canine rabies from 2006–2017, the implementation of rabies control interventions in different districts of Davao City in 2011–2017, and the impact of the IRCP and other factors that drive the reported cases. Since rabies is a notifiable disease (and underreporting is a major constraint in obtaining data of notifiable diseases), the quantitative

analysis relied heavily on reported cases. Descriptive statistics were utilized to analyze the trends and patterns of reported canine rabies cases along with other data sets provided by CVO. Moreover, count regression models were used to estimate the impact of government-initiated interventions. The implications of the findings will serve as a guide for the local government units in terms of crafting interventions that would mitigate rabies in their respective local units. Furthermore, the methodology of this paper can be applied to analyze reported rabies cases at a larger scale (i.e., regional or national).

MATERIALS AND METHODS

DataThe secondary data collected in this study include the following: (i) the monthly reported rabies cases in the dog population, (ii) the characteristics of monthly reported cases, and (iii) the weather conditions associated with the monthly reported rabies cases. All data pertaining to dog rabies cases were obtained from the CVO of Davao City, Philippines. Furthermore, the dataset obtained from the CVO is constrained by the submission of suspected samples from bite victims within the city. The weather data, on the other hand, were collected from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration – Region XI (PAGASA-XI) located also in Davao City. We only obtained the monthly reported rabies cases from January 2006 – December 2017 since acquisition of data prior to January 2006 is not possible. Moreover, at the time the study was conducted, only data up to December 2017 were available. Data pertaining to government-initiated interventions were also gathered. For instance, the monthly counts of castrated dogs, impounded dogs, and IEC sessions from January 2006 – December 2017 were collected. However, actual counts of vaccinated dogs from 2006–2010 are not available. Thus, instead of actual counts, the vaccination effort was considered. From an interview with the Research Head of the CVO, we know that the office intensified the implementation of the vaccination program in the year 2011; hence, we assumed vaccination effort as a binary variable. Specifically, it takes a value of 1 in months when the vaccination program was intensified i.e., 2011–2017, and 0 in months between 2006 and 2010 if otherwise.

Data for the characteristics of submitted dog samples could include dog gender, age, vaccination compliance, location, and ownership. However, data for dog gender, age, and vaccination compliance are sufficiently incomplete. These data sets were not included in the analytic impact assessment of control interventions but were used in evaluating the association of dog characteristics to positive

Lachica et al.: Trends of Rabies and Impact of IRCP in Davao City

Philippine Journal of ScienceVol. 148 No. 4, December 2019

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rabies detection testing by computing its odds ratio.

On the other hand, we used the complete data sets i.e., dog ownership and location, in determining the impact of dog characteristics on the monthly reported cases. Based on the data provided by the Davao CVO, dog ownership is classified into either owned or stray dogs (i.e., free-roaming ownerless dogs), which can be considered as a binary classification. However, there were months when the reported cases were coming from both owned dogs and stray dogs. Thus, dog ownership was then classified into three: owned dogs (all reported dogs are owned), stray dogs (all reported dogs are stray), and both owned and stray dogs. After processing the CVO dataset on dog ownership, we found that most of the monthly reported cases were classified as (1) owned dogs and (2) both owned and stray dogs. This implies that the stray dog classification contains many zeroes that can be contributing to collinearity. Consequently, we re-defined the dog classification variable as follows: if all reported dogs are owned, then it has a value of 1; else, if the reported dogs are composed of stray dogs or both owned and stray dogs, then a value of 0 is used. The latter is then interpreted as the monthly report in the presence of stray dogs.

Furthermore, we classified dog location according to the city congressional district i.e., Districts 1, 2, and 3 where the dog belongs to. However, there were months when the reported cases were coming from more than one district (i.e., the monthly reported case on positive coming from District 1 and 2). The proportion of the number of monthly reported cases that came from more than one district is 32.64%, while the proportion of the number of monthly reported cases that came from either one of the three districts is 36.8%. Knowing that the majority of monthly reports came from one district only, we assumed dog location as a binary variable where it takes the value of 0 when the reports of the positive rabies cases are coming from one district only; otherwise, 1. This assumption allows us to avoid collinearity problems, which are usually observed for polychotomous variables with too many levels. For example, if the location of the dog was considered to be a polychotomous variable, the following will be its level: District 1, District 2, District 3, Districts 1 and 2, Districts 1 and 3, Districts 2 and 3, and Districts 1, 2, and 3. District 1 implies that the monthly submitted samples were all coming from District 1. Districts 1 and 2 implies that the monthly submitted samples were coming from Districts 1 and 2.

The specific variable descriptions are presented in Table 1. Notice that, in the table, we displayed the mean of the binary variables (e.g., vaccinationeffort, interdist, owneddogs, presentstraydogs, and noreporteddogs). Here, the mean serves as a measure for how much ones

are present for a specific binary variable.

Statistical MethodsAssociation of dog characteristics to positive rabies detection. In determining the association of dog characteristics and positive rabies detection, the submitted samples were classified into groups – where a is the number of rabid dogs of exposed group, b is the number of non-rabid dogs of exposed group, c is the number of rabid dogs of non-exposed group, and d is the number of non-rabid dogs of non-exposed group. For instance, we calculate the odds ratio of positive rabies detection of male dogs to female dogs, then we consider male dogs to be the exposed group and the female group as the non-exposed group and vice versa.

The odds ratio of the exposed group to the non-exposed group is computed using the approach of Altman (1991) as shown in Equation 1:

Odds Ratio = adbc (1)

Furthermore, the 95% confidence interval (CI) of the computed odds ratio is computed as shown in Equation 2,

95% CI = eln(OR) ₋1.96 � SE(ln(OR)) to eln(OR)+1.96 � SE(ln(OR)) (2)

where SE{ln(OR)} = 1a + 1b + 1c + 1d

If OR > 1, then it indicates increased occurrence of positive rabies detection to the exposed group. If OR < 1, then it indicates decreased occurrence to the exposed group. On the other hand, if the OR = 1, then it indicates that the occurrence of the exposed group to the non-exposed group to rabies is the same.

Modeling count time-series. In this paper, we explored various count regression models to estimate the contributions of potential drivers for monthly rabies cases presented in Table 1. We discuss these models briefly in this section.

Generalized linear autoregressive moving average (GLARMA). The GLARMA is a sub-class of a generalized state-space model that can be used to detect and account for serial dependence in regression modeling of time-series data in the presence of a set of explanatory variables (Dunsmuir and Scott 2015). The checking of serial dependence is composed of the following steps: (1) identifying the orders of the autoregressive (p) and moving average (q) terms, (2) estimating tentative models from the identified p and q orders and testing for serial dependence of each model, and (3) identifying and diagnosis of the best model to assure compliance with model assumptions. The time-series data at hand are the rabies cases reported every month which are counts; hence, it is appropriate to analyze the data first



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using GLARMA models (Petukhova et al. 2018) to avoid spurious results when analyzing it using typical regression models, such as Poisson regression model and negative binomial regression model. By convention, the orders of p and q are identified by examining the autocorrelation function (ACF) and the partial autocorrelation function (PACF) plots (Mahan et al. 2015) of the stationary count time-series data. For instance, if p = 2, it implies that the rabies cases in the past two months explain the rabies cases in the current month. On the other hand, if q = 2, it implies that the average rabies cases within the last two months are affecting the rabies cases of the current month.

For the data at hand i.e., the monthly reported rabies cases (see Figure 1), we performed stationary [i.e., time-series data have constant mean and variance over time (Becketti 2013)] testing using the augmented Dickey-Fuller test at 0.05 level of significance. We then applied the Box-Cox transformation and data differencing in order to achieve data stationarity (Bhandari et al. 2017). For the case when the PACF and ACF plots have significant spikes at lags p and q, combinations of these lags were explored leading to tentative GLARMA(p,q) models. The lower order of p and q were considered initially; hence, we obtained tentative models such as GLARMA(0,q), GLARMA(p,0), and GLARMA(p,q) where p ≠ q. After estimating the model coefficients of a (p,q) model with lower q, we continued the analysis by increasing the order of q and running a new GLARMA(p,q) model ensuring that the same plots of model residuals were obtained (Dunsmuir and Scott 2015).

We checked for serial dependence in each tentative GLARMA(p,q) model statistically using the Likelihood Ratio and Wald tests. For statistical evidence of a GLARMA process, the GLARMA(p,q) model with the lowest AIC would be used to model the drivers of the monthly reported rabies cases; otherwise, parameter estimations would be done with usual count regression models. The checking of serial dependence and estimation of model parameters were implemented using the package glarma (Dunsmuir and Scott 2015) in R software version 3.5.1.

Table 1. Description and summary statistics of variables used in the study.

Variable name Description Mean Min Max

positiverabiescases The monthly number of dogs diagnosed to be positive to rabies 1.4583 0 6

vaccinationeffort The effort of the government in the implementation of the vaccination effort (0 = not intensified, 1 = intensified)

0.5833 0 1

castrateddogs The monthly number of dogs that were submitted for castration 13.1319 0 146

impoundeddogs The monthly number of dogs that were impounded by the CVO 248.1944 0 896

iecsessions The monthly number of IEC sessions that were conducted 23.0833 0 132

interdist The monthly reported dogs are coming from more than one district (0 = no, 1 = yes)

0.3264 0 1

owneddogsa A binary variable indicating 1 if all reported cases in a month are all owned dogs; otherwise, 0.

0.4931 0 1

presentstraydogs A binary variable indicating 1 if the reported cases in a month are from at least one stray dog; otherwise, 0.

0.2013 0 1

noreporteddogsb A binary variable indicating 1 if there are no reported dogs in a month; otherwise, 0.

0.3056 0 1

precipitationmm Monthly average rainfall 5.3629 0.4581 13.8871

humidity Monthly average relative humidity 81.1875 69 91

temperaturecelsius Monthly average temperature (°C) 28.2674 26.4 30.6a For count regression models: if 1, then all the reported dogs are owned, else the reported dogs are composed of both owned and stray dogs.b For GLARMA model: reference category of owneddogs and presentstraydogs so as to avoid the dummy variable trap.

Figure 1. Time-series trend of the monthly number of reported canine rabies cases (in black) and the total yearly rabies cases per year (in red) in Davao City, 2006–2017.



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Count Regression ModellingModel specification. The model specification used in this paper is shown in Equation 3:

0 i ii

R xβ β ε= + +∑ (3)

where R is the monthly reported rabies cases, xi is the numerical information of driver i (e.g., government-initiated interventions, dog characteristics, and weather conditions), the constant 𝛽0 and coefficients 𝛽i are the parameters to be estimated, and � is the error term.

Model parameter estimation. The coefficients of the parameters of Equation 3 were initially estimated following the Poisson regression framework. Poisson regression is the fundamental modeling tool for systems involving count observations, as suggested by Hilbe (2014).

Model diagnostics. It should be noted that over-dispersion (i.e., the actual variance is greater than the estimated variance) and excessive zeroes are usually encountered in the modeling process using Poisson regression. In this analysis, we found that 30.34% of the reported rabies cases contain zero values. The score test and Lagrange multiplier tests were performed at 5% level of significance to test whether over-dispersion is observed in the dataset. Meanwhile, the Vuong test was used to test whether a zero-inflated Poisson model is preferable to the traditional Poisson model (Hilbe 2014). Furthermore, the collinearity of the estimated model was also investigated by calculating the variance inflation factors (VIFs).

Addressing over-dispersion and excessive zeroes. Failure to account over-dispersion on a Poisson regression model would lead to spurious inferences as over-dispersion implies non-Poissonness (Burger et al. 2009). In addition, the presence of many legitimate zeroes violates the equality of mean and variance (Agrawal and Cockburn 2003). The tests described in the preceding section signifies that over-dispersion is present and that the presence of zeroes in the data is considered excessive. By re-estimating the coefficients of Equation 3, over-dispersion was dealt by adjusting the variance independently from the mean using the Negative binomial regression framework. Negative binomial is best used to account over-dispersion (Burger et al. 2009) as it relaxes the equi-dispersion restriction of the Poisson model (Greene 2008) to over-dispersed data (Ardiles et al. 2018) by introducing an over-dispersion parameter �. For example, α = 0.50 implies that the negative binomial model has accounted 0.50 extra variance in the dataset. Note that a Poisson model must be used when the parameter � of the Negative binomial model is not statistically different from zero. Furthermore, if given that a zero-inflated Poisson is preferable to Poisson, it is reasonable to assume that a zero-inflated

negative binomial will be preferable to a Negative binomial model whenever a Negative binomial model is preferred over a Poisson model. Lastly, we performed bootstrapping on the estimated zero-inflated negative binomial and obtained a bootstrapped standard error in reporting the estimated model (Hilbe 2014).

RESULTS AND DISCUSSION

Trends on Canine Rabies Cases Considering that government resources are limited, the previously mentioned interventions must be implemented using a strategic and optimal approach. Effective strategies must be identified and pursued, while recommendations can be formulated to improve those that are lacking. Hence, the impact of IRCP on the monthly reported rabies cases was quantitatively assessed using data obtained from the CVO on government-initiated interventions and dog characteristics, and the monthly weather conditions from the PAGASA-XI weather stations. A total of 144 observations for each quantity were collected covering January 2006 – December 2017.

Reported Rabies CasesIn Figure 1, there is a general decreasing trend on the number of reported cases in the period of 2011–2015 i.e., after the implementation of the IRCP, even though monthly fluctuations in rabies cases are also observed. This observation can be supported by the fact that the annual average of reported cases from 2006–2010 (21.60 dogs) is higher than that from 2011–2017 (approximately 14.57 dogs). However, this decline is disrupted in 2017 where an increase of up to six cases from 2016 was observed. The increasing cases of rabies in recent years could be interpreted in two (or both) ways: it may reflect a lapse on rabies control (Fisman et al. 2014), or it could have been due to the increase in reporting of the disease resulting from rabies awareness campaigns as the government is pushing forth the goal of making the Philippines rabies-free by 2020.

Association of Dog Characteristics to Rabies Positive DetectionThe characteristics of dog samples submitted to the local government for rabies testing were evaluated for association with rabies, as summarized in Table 2. Around half of the samples were male (55%), 35% were female, and the rest were unrecorded (10%), but the gender of the dog did not show any significant association with positive detection for rabies (p-value = 0.3). In terms of age, most of the samples were aged older than 3 mo (58%), a



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small proportion was up to 3 mo old (12%), and the rest were unrecorded (30%). Puppies up to 3 mo old were significantly more likely to test positive for rabies than older dogs (OR = 2.78, p-value = 0.0001). Furthermore, dogs usually receive vaccination when they are 3 mo or older. In terms of ownership, most received samples were owned dogs (88%), while the rest were stray dogs (12%), wherein the latter were also significantly more likely to test positive for rabies compared to the former (OR = 2.49, p-value = 0.0004). This may be attributed to the fact that vaccination compliance for stray dogs is relatively lower

than that with owned dogs. Hence, we expect a huge sampling bias in rabies detection with stray dogs. When the vaccination status of the dogs was examined, the majority were unvaccinated (76%), 10% were vaccinated, and 14% had no records. The vaccinated group appears to have a higher likelihood of rabies detection as compared to unvaccinated dogs (OR = 1.90, p-value = 0.02), and it should be noted that these data do not specify whether the dog’s vaccination was updated.

Geographic Distribution of Canine RabiesThe distribution of reported canine rabies cases in various districts of Davao City is illustrated in Figure 2. Urban districts such as Agdao and Poblacion, Buhangin, Bunawan, Calinan, Talomo, Toril, and Tugbok constantly had reported cases of canine rabies – with Buhangin having the highest cumulative rabies cases with 29 cases from 2011–2017. In contrast, the rural districts of Baguio and Paquibato had zero cases while Marilog had one case in 2017 (Figure 2). The densely populated residential areas of urban districts in the city make it more challenging for rabies control – especially since pets are a common feature in a Filipino household, pet ownership is not strictly regulated, and stray dogs are prevalent in the streets. However, underreporting of rabies cases in rural areas is also a possibility due to lack of public awareness or inaccessibility to veterinary or health centers.

Data Description of the IRCPThe local city government of Davao City has been implementing four intervention mechanisms since 2011 to mitigate canine rabies: vaccination, castration,

Table 2. Characteristics of dog samples in Davao City submitted for rabies examination from 2006–2017 and odds ratios for positive detection.

Dog characteristicsa

No. of samples (% of total)

No. of positive for

rabies (% of sub-population)

Odds OR (95% CI)

Gender

Male 331(55.4)

92(27.8)

0.38 0.82(0.56–1.20)

Female 207(34.6)

66(31.9)

0.47 1.22(0.83–1.78)

Unknown 60(10.0)

Age group***

≤ 3 mo 75(12.5)

36(48.0)

0.92 2.88(1.72–4.81)

> 3 mo 345(57.7)

86(24.9)

0.32 0.35(0.21–0.58)

Unknown 178(29.8)

Ownership**

Stray 70(11.7)

34(48.6)

0.94 2.49(1.50–4.13)

Owned 528(88.3)

145(27.5)

0.40 0.40(0.24–0.67)

Vaccination status**

Vaccinated 60(10.0)

29(48.3)

0.94 1.90(1.10–3.27)

Unvaccinated 455(76.1)

150(33.0)

0.49 0.53(0.31–0.90)

Unknown 83(13.9)

*p-value < 0.10 is significant at 10%, **p-value < 0.05 is significant at 5%, ***p-value < 0.01 is significant at 1%.

Figure 2. The distribution of reported canine rabies cases per district of Davao City, 2011–2017. Bars correspond to annual data starting in 2011 from left to right (in the order of 100).



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impounding, and IEC campaign. Figure 3 shows the time-series plot of the government-initiated interventions from 2011–2017 in Davao City. The proceeding sections will present the extent of implementation of these interventions by the local government.

VaccinationMass vaccination of dogs has been demonstrated to

recorded. Thus, the dog population was estimated using the human population in the city following the ratio of 1 dog to 10 individuals (Gsell et al. 2012; Davao CVO 2018, pers. comm., 30 July). As shown in Figure 5, the estimated vaccine coverage was constantly increasing since 2011 (except for a drop in 2015), but it was only in 2016 when the minimum effective coverage of 70% (Lavan et al. 2017) was almost achieved.

When the geographical distribution was evaluated, it appears that all districts in the city received the

Figure 3. The time-series plot of the government-initiated interventions from 2011–2017 in Davao City, Philippines.

be the most effective and inexpensive approach for rabies control (Lavan et al. 2017). Davao City has been implementing canine rabies vaccination but intensified this effort in 2011 by allotting an annual budget for the vaccines. Vaccines are distributed either through a central station such as the barangay center or via house-to-house visitation. Figure 4 shows the number of dogs that received vaccination per district from 2012–2017. There were no vaccination records available at district level prior to 2012. Vaccinations were done on a monthly schedule, with varying targets achieved per month. The government vaccinated on average 425 dogs/mo. There was a noticeable increase in the number of vaccinated dogs in 2016 (refer to the red time-series of Figure 3), although there was no decline in the reported cases for that year or the year after. Consequently, the vaccine coverage was next considered to assess the vaccination effort of the government. The vaccine coverage was computed by getting the proportion of vaccinated dogs to the dog population. However, there is no exact dog population

Figure 4. The number of dogs vaccinated per district of Davao City, 2012–2017. The number of vaccinated dogs (in the order of 104).

Figure 5. The estimated vaccination coverage of the CVO in Davao City, 2011–2017.



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vaccination, but the number of vaccinated dogs varied widely (Figure 4). The rural districts such as Baguio, Marilog, and Paquibato constantly had the lowest number of vaccinations, while the remaining urban districts had relatively higher levels.

CastrationLimiting the dog population does not only help reduce animal rabies transmission, but it also eases the target required for an optimal rabies vaccination coverage (Taylor et al. 2017). A humane approach for dog population control is through sterilization methods such as castration. Figure 6 shows the number of animals subjected to the local government’s castration program from 2011–2017 on a monthly basis. An average of 29 dogs was castrated per month, which is almost 15-fold lower than the monthly average of vaccinated dogs. The levels fluctuated over time, although the numbers remained small relative to vaccination, and no trend was observed (refer to the blue time-series of Figure 3). In terms of distribution, the urban districts had relatively higher numbers of castrated dogs except for Bunawan (Figure 6).

specifically to reduce the stray dog population (Taylor et al. 2017). The impounding program of the city was launched in December 2012. The average number of impounded dogs from 2012–2017 was 428/mo, with a significant increase observed in 2014 followed by a decreasing trend until the end of 2017 (refer to the yellow time-series of Figure 3).

IEC SessionsConducting an IEC session is crucial for promoting awareness and action on public health-related issues. There is an increasing trend for the number of IEC sessions conducted by the local government regarding rabies since 2011, with an average of around 39 sessions/mo (refer to the yellow time-series of Figure 3). At the district level, the number of participants in the IEC sessions widely ranged from 50–2,969 – with Baguio, Calinan, Tugbok, and Paquibato having very low turnouts (Figure 7). Hence, the local government should also consider targeting a larger audience per location to maximize information dissemination within the city.

Figure 6. The number of castrated dogs per district of Davao City, 2012–2017. The number of castrated dogs (in the order of 102).

ImpoundingAnother approach to dog population control implemented in Davao City is impounding, wherein free-roaming dogs are caught and taken to the city pound. Dogs that are not claimed within three days are euthanized. This model has been commonly adopted in developed countries and is intended

Figure 7. The number of IEC sessions per district of Davao City, 2012–2017. The number of people attended in the IEC sessions per district (in the order of 103).

Impact of the IRCPDue to the time-series nature of the rabies data (e.g., monthly submission), it was first analyzed using the GLARMA framework (Dunsmuir and Scott 2015).

GLARMA (p,q) SpecificationsThe PACF plots of the stationary data (Figure 8) showed significant spikes at lags 3 and 7, indicating that the autoregressive order (p) is 3 or 7. The autocorrelation plots



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also showed significant spikes at lags 3 and 7, indicating that the moving average order of (q) is 3 or 7. A total of 36 GLARMA specifications (Appendix Table I) were estimated. However, all of the GLARMA frameworks revealed that there is no statistical evidence for serial dependence on the monthly reported rabies cases.

Count Regression ModelsCount regression models were estimated since the nature of the outcome variable, which is the monthly reported cases, is measured in counts. Based on the model diagnostics (Appendix Table II) and information criteria (Appendix Table III), the zero-inflated negative binomial model was the best model to use in this analysis. Table 3 displays the zero-inflated negative binomial regression model that models the relationship of the control interventions made by the government and other variables (weather and dog characteristics) to the monthly reported rabies. Furthermore, the calculated value of the mean variance inflation factor is 2.27, which is less than 10, indicating no degrading collinearity (Kennedy 2008).

The analysis points to a number of factors that drive rabies cases in Davao City. There is a reduction of 0.371 reported cases if the cases come from owned dogs relative to when stray dogs are included among the cases, suggesting the role of stray dogs as important vectors of the disease (Otranto et al. 2017, Miró et al. 2017), which is supported by the earlier findings in this study. This is further reinforced by the findings on precipitation and location (Raghavan et al. 2016). The average precipitation in Davao City is around 6 mm, as reflected in the data collected from the PAGASA-XI. A 1 mm rise in precipitation will decrease reported cases by 0.417 (Table 2), as rains are expected to discourage the movement of stray dogs as well as the rabid dogs e.g., inaccessibility of road networks (Sambo et al. 2013). Rain serves as an important factor in predicting the incidence of rabies in various species [e.g., arctic and red foxes (Kim et al. 2014), as well as canine dogs (Rezaeinasab et al. 2007)]. In addition, rabid dogs are afraid of water (Kurosawa et al. 2017). However, it also very likely that there is less reporting during months with high precipitation because the data collected are received samples. The location of the

Figure 8. PACF and ACF plots for determining the values of p and q respectively for model (p,q) identification.



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Table 3. Coefficient and marginal effect estimate of the factors on monthly reported rabies cases using zero-inflated negative binomial regression modela.

Explanatory variables Coefficient Standard errorsb p-valuec Marginal effect p-value

Control interventions

vaccinationeffort 0.0774 0.1801 0.1921 0.6670 0.1131 0.6690

castrateddogs 0.0005 0.0021 0.0024 0.8090 0.0007 0.8090

impoundeddogs –0.0006 0.0003 0.0003 0.0390** –0.0008 0.0420**

iecsessions 0.0003 0.0017 0.0020 0.8440 0.0005 0.8440

Dog characteristics

interdistrict 0.7095 0.0855 0.0896 0.0000*** 1.0347 0.0000***

ownedonly –0.2544 0.0923 0.0995 0.0060** –0.3710 0.0080**

Weather conditions

precipitation (mm) –0.0286 0.0152 0.0167 0.0610* –0.0417 0.0620*

humidity (%) 0.0026 0.0116 0.0147 0.8240 0.0037 0.8250

temperature (0C) 0.0434 0.0613 0.0698 0.4790 0.0633 0.4800

Constant –0.7048 2.2253 2.7189 0.7510

Inflate

(noreporteddogs) 48.6208 0.1856 7.3807 0.0000***

constant –22.3130 0.1003 3.7710 0.0000***

/lnalpha –19.8208 0.0822 2531.8980 0.9940

alpha 2.47x10–9 2.03x10–10 6.24x10–6 2.90x10–9***aMean VIF = 2.27 implies no degrading collinearity.b Left standard errors are the robust standard errors while the right standard errors are the bootstrapped (n = 1000) standard errors.c If significant: *p-value < 0.10 is significant at 10%, **p-value < 0.05 is significant at 5%, ***p-value < 0.01 is significant at 1%.

reported cases was also found to be significantly related to the monthly reported cases. This means that relative to cases found to a district, the monthly report will increase if the reported cases are coming from more than one district. This suggests that the spatial spread of the disease such as through natural (Belik et al. 2011) or human-mediated (Morters et al. 2014), dog movement should be further studied (Balcan et al. 2010).

Among the four interventions implemented by the local government, only one was identified as having a statistically significant drive to the reported cases. In relation to the findings that stray dogs are important vectors of the disease, stray dog population control through impounding was deemed significant (Jackman and Rowan 2007). When the dog population is large, the spread of rabies virus is more likely to happen. Impounding, on the other hand, removes dogs in the population. Hence, when a stray dog is exposed to the virus (contains the virus but is not yet contagious), removing it will curb its potential to spread the virus. For every one impounded dog, the rabies case will decrease by 0.0008 i.e., at least 1,250 dogs are needed to be impounded to ensure a reduction of one reported rabid dog. This may not be practical in terms of

resources, but these findings point to stray dog population control as an effective strategy for rabies control (Jackman and Rowan 2007). Less resource-demanding and humane alternatives can be simultaneously explored such as adoption programs, street vaccination with tagging, and castration of stray dogs.

In contrast, vaccination, castration, and IEC were not found to have an impact on rabies cases in Davao City. This could be attributed to the low vaccination coverage, unequal distribution of vaccines among barangays (Figure 4) – possibly due to budget constraints (Jackman and Rowan 2007, Schildecker et al. 2017) – and non-compliance with vaccination updates [e.g., to poor timing of the intervention (Bardosh et al. 2014)].

On the other hand, implementing a city-wide castration program appears to be challenging as evidenced by the low turnout of castrated dogs in the city, either due to poor public awareness or the technical and financial demands of the program. Finally, IEC sessions need to be improved in terms of the number of participants.



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CONCLUSIONThis study has provided insights into the trends and patterns of monthly reported rabies cases and examined the impact of the government-initiated interventions on rabies cases in Davao City. Stray dogs i.e., ownerless and free-roaming dogs, have been identified as a driver of rabies transmission. Reported rabies cases are expected to drop during rainy season when the movement of stray dogs becomes restricted. Among the government-initiated interventions, the impounding program has been found to be significantly associated with the declining number of reported rabies cases. Hence, this study suggests continuing the city’s impounding efforts especially during the summer season, but it should be noted that there are concerns on the long-term sustainability of dog-impounding as it can be resource-demanding and may cause instability in the dog population. Furthermore, this study recommends that the vaccination efforts must be increased such that coverage will be at least 70%. Thus, the roles of impounding and increasing vaccination coverage are crucial in controlling rabies. The implementation of other interventions such as castration and IEC sessions must also be further reviewed.

ACKNOWLEDGMENTSThis research was supported by the Local Government of Davao City with Project Code ADF 2018 and the Commission on Higher Education: Discovery Applied Research and Extension for Trans/Inter-Disciplinary Opportunities (CHED DARE-TO) 2017 Research Grant through the Synoptic Study on Transmission and Optimum Control to Prevent Rabies (STOP) Rabies research program. The authors also thank Dr. Gloria N. Marquez, Arlene Lagare, Janice H. Mendoza, and Ma. Noreen J. Eng from the Davao CVO for their assistance in data collection.

CONFLICT OF INTERESTThe authors declare that they have no conflict of interest.

NOTES ON APPENDICES The complete appendices section of the study is accessible at http://philjournsci.dost.gov.ph

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Table I. Summary of GLARMA models from (p,q) tentative models.

GLARMA (p,q)a Link function Residual type Likelihood ratio test Wald test Serial dependence

GLARMA (0,3) Poisson Pearson 0.6260 0.6080 AbsentGLARMA (0,3) Poisson Score-type 0.8870 0.8800 AbsentGLARMA (0,3)b Poisson Identity na na naGLARMA (0,3)b Negative binomial Pearson na na naGLARMA (0,3)b Negative binomial Score-type na na naGLARMA (0,3)b Negative binomial Identity na na naGLARMA (3,0) Poisson Pearson 0.6470 0.6480 AbsentGLARMA (3,0) Poisson Score-type 0.9030 0.9110 AbsentGLARMA (3,0)b Poisson Identity na na naGLARMA (3,0)b Negative binomial Pearson na na naGLARMA (3,0)b Negative binomial Score-type na na naGLARMA (3,0)b Negative binomial Identity na na naGLARMA (0,7) Poisson Pearson 0.9710 0.9680 AbsentGLARMA (0,7) Poisson Score-type 0.9460 0.9420 AbsentGLARMA (0,7)b Poisson Identity na na naGLARMA (0,7)b Negative binomial Pearson na na naGLARMA (0,7)b Negative binomial Score-type na na naGLARMA (0,7)b Negative binomial Identity na na naGLARMA (7,0) Poisson Pearson 0.9760 0.9790 AbsentGLARMA (7,0) Poisson Score-type 0.9540 0.9570 AbsentGLARMA (7,0)b Poisson Identity na na naGLARMA (7,0)b Negative binomial Pearson na na naGLARMA (7,0)b Negative binomial Score-type na na naGLARMA (7,0)b Negative binomial Identity na na naGLARMA (3,7) Poisson Pearson 0.8920 0.8860 AbsentGLARMA (3,7) Poisson Score-type 0.9910 0.9930 AbsentGLARMA (3,7) Poisson Identity na na naGLARMA (3,7)b Negative binomial Pearson na na naGLARMA (3,7)b Negative binomial Score-type na na naGLARMA (3,7)b Negative binomial Identity na na naGLARMA (7,3) Poisson Pearson 0.8640 0.8110 AbsentGLARMA (7,3) Poisson Score-type 0.9880 0.9880 AbsentGLARMA (7,3) Poisson Identity na na naGLARMA (7,3)b Negative binomial Pearson na na naGLARMA (7,3)b Negative binomial Score-type na na naGLARMA (7,3)b Negative binomial Identity na na na

ap ≠ q as suggested by Dunsmuir and Scott (2015)bThe GLARMA (p,q) model was not built.

APPENDICESAppendix I. Testing for serial dependence via GLARMA Framework: stationarity testing using the augmented Dickey-Fuller test.

If p-value < 0.05, reject the null hypothesis. Since p-value > 0.05, the data is non-stationary at level.

Augmented Dickey-Fuller testData: Monthly reported rabies casesDickey-Fuller = –3.3233, lag order = 5, p-value = 0.07016Alternative hypothesis: stationary

Appendix II. Testing for serial dependence via GLARMA Framework: stationarity testing using the augmented Dickey-Fuller test.

If p-value < 0.05, reject the null hypothesis. Since p-value < 0.05, the Box-Cox transformed data is now stationary and is qualified for serial-dependence analysis.

Augmented Dickey-Fuller testData: Box-Cox transformed monthly reported rabies casesDickey-Fuller = –3.924, lag order = 5, p-value = 0.01497Alternative hypothesis: stationary



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Table II. Formal testing for data over-dispersion and zero-inflation of the estimated traditional Poisson model.

Test statistic Computed value p-value Decision

Score testa –0.1826 0.0040 Reject Ho

Lagrange multiplier testa 76.1250 2.6630x10–18 Reject Ho

Voung testb 9.25 0.0000 Reject H0

aTest for over-dispersion with Ho: Dispersion is zero. bTest for ZI-Poisson versus traditional Poisson with Ho: ZI-Poisson is not preferable to the traditional Poisson (i.e., the number of zero counts do not exceed Poisson distributional assumptions) (Hilbe 2014).

Table III. Comparison of count regression models considered in this paper.

Positive rabies cases

Standard Poisson regression Zero-inflated negative binomial regressiona

Coefficients p-valueb Coefficients p-value

Government-initiated interventions

Vaccinationeffort(0 = No, 1 = Yes) 0.0990 0.7370 0.0774 0.6670

castrateddogs –0.0026 0.3890 0.0005 0.8090

impoundeddogs –0.0007 0.1670 –0.0006 0.0390**

IECsessions 0.0012 0.6820 0.0003 0.8440

Dog characteristics

Interdistrict (0 = No, 1 = Yes) 1.3144 0.0000*** 0.7095 0.0000***

Ownedonly (0 = No, 1 = Yes) 0.3277 0.0180** –0.2544 0.0060***

Weather conditions

precipitationmm –0.0020 0.9310 –0.0286 0.0610

humidity –0.0350 0.0440 0.0026 0.8240

temperaturecelsius 0.0099 0.9120 0.0698 0.4790

_cons 2.2246 0.4760 2.7189 0.7510

Inflation variable (NoReportedDogs) NA NA 48.6208 0.0000***

alpha NA NA 2.47 x 10–9 2.03 x 10–10

Wald chi2(9) 194.11 188.78

p-value 0.0000*** 0.0000***

AIC 381.6063 303.8352

BIC 411.3045 342.4428a44 out 145 (~ 30.34%) monthly observations had zero reported case.bIf significant: *p-value < 0.10 is significant at 10%, **p-value < 0.05 is significant at 10%, ***p-value < 0.01 is significant at 10%



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