## References

1.Baer, A, Rodriguez, CV and Duchin, JS (2011) An automated system for public health surveillance of school absenteeism. Journal of Public Health Management and Practice 17, 59–64.

2.Xu, W et al. (2017) Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China. PLoS ONE 12, e0184527.

3.Cheng, CKY, Channarith, H and Cowling, BJ (2013) Potential use of school absenteeism record for disease surveillance in developing countries, case study in rural Cambodia. PLoS ONE 8, e76859.

4.Mandl, KD et al. (2004) Implementing syndromic surveillance: a practical guide informed by the early experience. Journal of the American Medical Informatics Association 11, 141–150.

5.Fan, Y et al. (2014) Estimating the effectiveness of early control measures through school absenteeism surveillance in observed outbreaks at rural schools in Hubei, China. PLoS ONE 9(9), e106856.

6.Kara, EO et al. (2012) Absenteeism in schools during the 2009 influenza A (H1N1) pandemic: a useful tool for early detection of influenza activity in the community? Epidemiology and Infection 140, 1328–1336.

7.Calvin, KYC et al. (2012) Electronic school absenteeism monitoring and influenza surveillance, Hong Kong. Emerging Infectious Disease Journal 18, 885.

8.Crawford, GB et al. (2011) Influenza and school-based influenza-like illness surveillance: a pilot initiative in Maryland. Public Health Reports 126, 591–596.

9.Moghimbeigi, A et al. (2009) A score test for zero-inflation in multilevel count data. Computational Statistics & Data Analysis 53, 1239–1248.

10.Lambert, D (1992) Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics 34, 1–14.

11.Lee, AH et al. (2005) Modelling bivariate count series with excess zeros. Mathematical Biosciences 196, 226–237.

12.Malesios, C et al. (2016) Spatio-temporal modelling of foot-and-mouth disease outbreaks. Epidemiology and Infection 144, 2485–2493.

13.Bandyopadhyay, D et al. (2011) Some considerations for excess zeroes in substance abuse research. American Journal of Drug and Alcohol Abuse 37, 376–382.

14.Stroup, WW (2012) Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. Boca Raton: CRC Press, pp. 361–374.

15.Wang, J, Xie, H and Fisher, JF (2011) Multilevel Models: Applications Using SAS. Berlin: Higher Education Press, pp. 175–187.

16.Rose, CE et al. (2006) On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data. Journal of Biopharmaceutical Statistics 16, 463–481.

17.Min, Y and Agresti, A (2005) Random effect models for repeated measures of zero-inflated count data. Statistical Modelling 5, 1–19.

18.Monod, A (2014) Random effects modeling and the zero-inflated Poisson distribution. Communications in Statistics – Theory and Methods 43, 664–680.

19.Neelon, BH, O'Malley, AJ and Normand, S-LT (2010) A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use. Statistical Modelling 10, 421–439.

20.Xie, H et al. (2004) A method for analyzing longitudinal outcomes with many zeros. Mental Health Services Research 6, 239–246.

21.Huirong, Z, Sheng, L and Stacia, MD (2015) Zero-inflated count models for longitudinal measurements with heterogeneous random effects. Statistical Methods in Medical Research 26, 1774–1786.

22.Yan, W et al. (2013) ISS – an electronic syndromic surveillance system for infectious disease in rural China. PLoS ONE 8, e62749.

23.Yan, WR et al. (2012) Establishing a web-based integrated surveillance system for early detection of infectious disease epidemic in rural China: a field experimental study. BMC Medical Informatics and Decision Making 12, 1–7.

24.Fang, R et al. (2016) Zero-inflated negative binomial mixed model: an application to two microbial organisms important in oesophagitis. Epidemiology and Infection 144, 2447–2455.

25.Zuur, AF et al. (2009) Mixed Effects Models and Extensions in Ecology with R – Chapter 11 Zero-Truncated and Zero-Inflated Models for Count Data. New York: Springer, pp. 261–294.

26.Hagen, KS et al. (2014) Assessing the early aberration reporting system's ability to locally detect the 2009 influenza pandemic. Statistics Politics & Policy 2, 30.

27.Fearnley, L (2008) Signals come and go: syndromic surveillance and styles of biosecurity. Environment and Planning A 40, 1615.

28.Fouillet, A et al. (2013) Guidelines for implementing syndromic surveillance in Europe and proposal for a European syndromic surveillance strategy. European Journal of Public Health 23(Suppl. 1), ckt126.117.

29.Arab, A (2015) Spatial and spatio-temporal models for modeling epidemiological data with excess zeros. International Journal of Environmental Research and Public Health 12, 10536–10548.

30.Mullahy, J (1986) Specification and testing of some modified count data models. Journal of Econometrics 33, 341–365.

31.Heilbron, DC (1994) Zero-altered and other regression models for count data with added zeros. Biometrical Journal 36, 531–547.

32.Neelon, B, O'Malley, AJ and Smith, VA (2016) Modeling zero-modified count and semicontinuous data in health services research. Part 1: Background and overview. Statistics in Medicine 35, 5070–5093.

33.Neelon, B, O'Malley, AJ and Smith, VA (2016) Modeling zero-modified count and semicontinuous data in health services research part 2: case studies. Statistics in Medicine 35, 5094–5112.

34.Triple, SP (2011) Assessment of syndromic surveillance in Europe. The Lancet 378, 1833–1834.

35.Hutwagner, LC et al. (2005) A simulation model for assessing aberration detection methods used in public health surveillance for systems with limited baselines. Statistics in Medicine 24, 543–550.

36.Jang, H, Lee, S and Kim, SW (2010) Bayesian analysis for zero-inflated regression models with the power prior: applications to road safety countermeasures. Accident Analysis & Prevention 42, 540–547.

37.Blangiardo, M and Cameletti, M (2015) Spatial and Spatio-Temporal Bayesian Models with R-INLA. Chichester, UK: John Wiley & Sons.