Public health monitoring using control charts based on convex hull

References

• Agarwal M, Gupta SKr, Biswas KK. 2020. Development of efficient CNN model for tomato crop disease identification. Sustain Comput Informatics Syst. 28:100407.
   Web of Science ®Google Scholar
• Alawiyah M, Johar D, Ruchjana B. 2021. Homogeneous poisson process in daily case of covid-19. J Phys Conf Ser. 1722:012078.
   Google Scholar
• Anderson N, Titterington D. 1997. Some methods for investigating spatial clustering, with epidemiological applications. J R Stat Soc Series A (Stat Soc) 160(1):87–105.
   Google Scholar
• Bersimis S, Economou P. 2017. The use of length-biased distributions in statistical monitoring. Aust N Z J Stat. 59(2):155–167.
   Web of Science ®Google Scholar
• Bersimis S, Sachlas A. 2022. Surveilling public health through statistical process monitoring: a literature review and a unified framework. Commun Stat Case Stud Data Anal Appl. 8(3):515–543.
   Google Scholar
• Bersimis S, Triantafyllopoulos K. 2020. Dynamic non-parametric monitoring of air-pollution. Methodol Comput Appl Probab. 22:1457–1479.
   Web of Science ®Google Scholar
• Bersimis S, Chalkias C, Anthopoulou T. 2014. Detecting and interpreting clusters of economic activity in rural areas using scan statistic and lisa under a unified framework. Appl Stoch Models Bus Ind. 30(5):573–587.
   Web of Science ®Google Scholar
• Bersimis S, Sgora A, Psarakis, S. 2016. The application of multivariate statistical process monitoring in non-industrial processes. Qual Technol Quant Manag. 15:526–549.
   Web of Science ®Google Scholar
• Bersimis S, Sachlas A, Sparks R. 2017a. Performance monitoring and competence assessment in health services. Methodol Comput Appl Probab. 19(4):1169–1190.
   Web of Science ®Google Scholar
• Bersimis S, Degiannakis S, Georgakellos D. 2017b. Real-time monitoring of carbon monoxide using value-at-risk measure and control charting. J Appl Stat. 44(1):89–108.
   Web of Science ®Google Scholar
• Bhowmick T, Griffin A, Maceachen A, Kluhsman B, Lengerich EL. 2008. Informing geospatial toolset design: Understanding the process of cancer data exploration and analysis. Health Place 14(3):576–607.
   PubMed Web of Science ®Google Scholar
• Blangiardo M, Boulieri A, Diggle P, Piel FB, Shaddick G, Elliott P. 2020. Advances in spatiotemporal models for non-communicable disease surveillance. Int J Epidemiol. 49(Supplement_1):i26–i37.
   PubMedGoogle Scholar
• Boutsikas M, Koutras M. 2003. Bounds for the distribution of two-dimensional binary scan statistics. Probab Eng Inf Sci. 17(4):509–525.
   Web of Science ®Google Scholar
• Bracher J, Held L. 2022. Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction. Int J Forecast. 38(3):1221–1233.
   Web of Science ®Google Scholar
• Bravata D, McDonald K, Smith W, Rydzak C, Szeto H, Buckeridge DL, Haberland C, Owens DK. 2004. Systematic review: surveillance systems for early detection of bioterrorism-related diseases. Ann Internal Med. 140(11):910–922.
   PubMed Web of Science ®Google Scholar
• Caldwell G. 1989. Time-space cancer clusters. Health Environ Digest. 3(5):1–3.
   Google Scholar
• Charu V, Zeger S, Gog J, Bjørnstad ON, Kissler S, Simonsen L, Grenfell BT, Viboud C. 2017. Human mobility and the spatial transmission of influenza in the United States. PLoS Comput Biol. 13(2):e1005382.
   PubMed Web of Science ®Google Scholar
• Chirico M. 2020. Emergency - 911 calls. Accessed: 2013-06-24.
   Google Scholar
• Correia F, Neveda R, Oliveira P. 2011. Chronic respiratory patient control by multivariate charts. Int J Health Care Qual Assur. 24(8):621–643.
   PubMedGoogle Scholar
• Cressie N. 1994. Models for spatial processes. In: Stanford JL, Vardeman SB, editors. Statistical Methods for Physical Science, volume 28 of Methods in Experimental Physics. New York: Academic Press. p. 93–124.
   Google Scholar
• Darling R, Waterman M. 1985. Matching rectangles in d-dimensions: algorithms and laws of large numbers. Adv Math. 55(1):1–12.
   Web of Science ®Google Scholar
• Diggle PJ, Besag J, Gleaves JT. 1976. Statistical analysis of spatial point patterns by means of distance methods. Biometrics 32:659–667.
   Web of Science ®Google Scholar
• Duczmal L, Kulldorff M, Huang L. 2006. Evaluation of spatial scan statistics for irregularly shaped clusters. J Comput Graph Stat. 15(2):428–442.
   Web of Science ®Google Scholar
• Dworkis DA, Marvel J, Sanossian N, Arora S. 2020. Neighborhood-level stroke hot spots within major united states cities. Amer J Emer Med. 38(4):794–798.
   PubMed Web of Science ®Google Scholar
• Fanaee-T H, Gama J. 2015. Eigenspace method for spatiotemporal hotspot detection. Expert Syst. 32(3):454–464.
   Web of Science ®Google Scholar
• Fricker R, Chang J. 2008. A spatio-temporal methodology for real-time biosurveillance. Qual Eng. 20(4):465–477.
   Google Scholar
• Frisén M, Andersson E, Schiöler L. 2010. Evaluation of multivariate surveillance. J Appl Stat. 37(12):2089–2100.
   Web of Science ®Google Scholar
• Getz W, Wilmers C. 2004. A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography 27(4):489–505.
   Web of Science ®Google Scholar
• Glaz J, Naus J, Wallenstein S. 2001. Scan statistics. New York: Springer.
   Google Scholar
• Goshtasby A, Stockman GC. 1985. Point pattern matching using convex hull edges. IEEE Trans Syst Man Cybern SMC-15(5):631–637.
   Web of Science ®Google Scholar
• Griesser M, Barnaby J, Schneider N, Figenschau N, Wright J, Griffith S, Kazem A, Russell A. 2009. Influence of winter ranging behaviour on the social organization of a cooperatively breeding bird species, the apostlebird. Ethology 115(9):888–896.
   Web of Science ®Google Scholar
• Haiman G. 2000. Estimating the distributions of scan statistics with high precision. Extremes 3(4):349–361.
   Google Scholar
• Held L, Paul M. 2012. Modeling seasonality in space-time infectious disease surveillance data. Biom J. 54(6):824–843.
   PubMed Web of Science ®Google Scholar
• Hjalmars U, Kulldorff M, Gustafsson G, Nagarwalla N. (1996). Childhood leukaemia in Sweden: using gis and a spatial scan statistic for cluster detection. Stat Med. 15(7–9):707–715.
   PubMed Web of Science ®Google Scholar
• Jahn M, Weiß, CH, Kim H-Y. 2023. Approximately linear ingarch models for spatio-temporal counts. J R Stat Soc Series C Appl Stat. 72(2):476–497.
   Google Scholar
• Jesri N, Saghafipour A, Koohpaei A, Farzinnia B, Jooshin MK, Abolkheirian S, Sarvi M. 2021. Mapping and spatial pattern analysis of covid-19 in Central Iran using the local indicators of spatial association (lisa). BMC Public Health 21:1–10.
   PubMed Web of Science ®Google Scholar
• Kalgonda A, Kulkarni S. 2004. Multivariate quality control chart for autocorrelated processes. J Appl Stat. 31(3):317–327.
   Web of Science ®Google Scholar
• Kammerer S, Shang N, Althomsons S, Haddad M, Grant J, Navin T. 2013. Using statistical methods and genotyping to detect tuberculosis outbreaks. Int J Health Geograph. 12(1):15–22.
   PubMedGoogle Scholar
• Kleinman K, Lazarus R, Platt R. 2004. A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. Amer J Epidemiol. 159(3):217–224.
   PubMed Web of Science ®Google Scholar
• Kman N, Bachmann D. 2012. Biosurveillance: a review and update. Adv Prevent Med. 2012:301408.
   PubMedGoogle Scholar
• Koutras M, Bersimis S, Antzoulakos D. 2006. Improving the performance of the chi-square control chart via runs rules. Methodol Comput Appl Probab. 8:409–426.
   Web of Science ®Google Scholar
• Kristoffersson A, Lindén M. 2020. Wearable sensors for monitoring and preventing noncommunicable diseases: a systematic review. Information 11(11):521.
   Google Scholar
• Kroll M, Phalkey RK, Kraas F. 2015. Challenges to the surveillance of non-communicable diseases–a review of selected approaches. BMC Public Health 15(1):1–12.
   PubMedGoogle Scholar
• Kulldorff M. 1997. A spatial scan statistic. Commun Stat Theory Methods 26(6):1481–1496.
   Web of Science ®Google Scholar
• Kulldorff M. 2001. Prospective time periodic geographical disease surveillance using a scan statistic. J R Stat Soc Series A Stat Soc. 164(1):61–72.
   Web of Science ®Google Scholar
• Kulldorff M, Nagarwalla N. 1995. Spatial disease clusters: detection and inference. Stat Med. 14(8):799–810.
   PubMed Web of Science ®Google Scholar
• Kulldorff M, Feuer E, Miller B, Freedma L. 1997. Breast cancer clusters in the Northeast United States: a geographic analysis. Amer J Epidemiol. 146(2):161–170.
   PubMed Web of Science ®Google Scholar
• Laurentini A. 1994. The visual hull concept for silhouette-based image understanding. IEEE Trans Pattern Anal Mach Intel. 16(2):150–162.
   Web of Science ®Google Scholar
• Lee M. 2009. An enhanced convex-hull edge method for flatness tolerance evaluation. Comput-Aided Des. 41(12):930–941.
   Web of Science ®Google Scholar
• Mahoney P, Young J. 2017. Uncovering behavioural states from animal activity and site fidelity patterns. Methods Ecol Evol. 8(2):174–183.
   Web of Science ®Google Scholar
• Marshall R. 1991. A review of methods for the statistical analysis of spatial patterns of disease. J R Stat Soc Series A (Stat Soc) 154(3):421–441.
   Web of Science ®Google Scholar
• Moitra P, Sinha S. 2019. Localized spatial distributions of disease phases yield long-term persistence of infection. Sci Rep. 9(1):20309.
   PubMedGoogle Scholar
• Naus J. 1965. Clustering of random points in two dimensions. Biometrika 52:263–267.
   Web of Science ®Google Scholar
• Pannacci E, Farneselli M, Guiducci M, Tei F. 2020. Mechanical weed control in onion seed production. Crop prot. 135:105221.
   Web of Science ®Google Scholar
• Park J, Yoon S, Na M, Song H. 2015. The effects of air pollution on mortality in South Korea. Proc Environ Sci. 26:62–65.
   Google Scholar
• Rakhmatulin I, Kamilaris A, Andreasen C. 2021. Deep neural networks to detect weeds from crops in agricultural environments in real-time: a review. Remote Sens. 13(21):4486.
   Google Scholar
• Rehman TU, Zaman QU, Chang YK, Schumann AW, Corscadden KW. 2019. Development and field evaluation of a machine vision based in-season weed detection system for wild blueberry. Comput Electron Agric. 162:1–13.
   Web of Science ®Google Scholar
• Rigby R, Stasinopoulos D. 2006. Using the box-cox t distribution in gamlss to model skewness and kurtosis. Stat Model. 6(3):209–229.
   Web of Science ®Google Scholar
• Rigby RA, Stasinopoulos DM. 2005. Generalized additive models for location, scale and shape, (with discussion). Appl Stat. 54:507–554.
   Web of Science ®Google Scholar
• Rogerson P. 2005. Spatial surveillance and cumulative sum methods. Hoboken, NJ: Wiley-Blackwell. p. 95–114.
   Google Scholar
• Rogerson P, Yamada I. 2004. Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches. Stat Med. 23(14):2195–2214.
   PubMed Web of Science ®Google Scholar
• Rolka H, Burkom H, Cooper G, Kulldorff M, Madigan D, Wong W. 2007. Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs. Stat Med. 26(8):1834–56.
   PubMed Web of Science ®Google Scholar
• Rosin P. 2006. Training cellular automata for image processing. In: Kalviainen H, Parkkinen J, Kaarna A, editors. Image Analysis. Berlin, Heidelberg: Springer. p. 195–204.
   Google Scholar
• Rossi G, Lampugnani L, Marchi M. 1999. An approximate cusum procedure for surveillance of health events. Stat Med. 18(16):2111–2122.
   PubMed Web of Science ®Google Scholar
• Royston P, Altman D. 1994. Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling. J R Stat Soc Series C (Appl Stat) 43(3):429–467.
   Web of Science ®Google Scholar
• Sachlas A, Bersimis S, Psarakis S. 2019. Risk-adjusted control charts: theory, methods, and applications in health. Stat Biosci. 11:630–658.
   Web of Science ®Google Scholar
• Sauerbrei W, Royston P. 1999. Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials. J R Stat Soc Series A (Stat Soc) 162(1):71–94.
   Google Scholar
• Schiöler L, Frisén M. 2012. Multivariate outbreak detection. J Appl Stat. 39(2):223–242.
   Web of Science ®Google Scholar
• Shmueli G, Burkom H. 2010. Statistical challenges facing early outbreak detection in biosurveillance. Technometrics 52(1):39–51.
   Web of Science ®Google Scholar
• Sim CH, Lim MH. 2008. Attribute charts for zero-inflated processes. Commun Stat Simul Comput. 37(7):1440–1452.
   Web of Science ®Google Scholar
• Sowmyanarayanan T, Mukhopadhya A, Gladstone B, Sarkar R, Kang G. 2008. Investigation of a hepatitis a outbreak in children in an urban slum in Vellore, Tamil Nadu, using geographic information systems. Indian J Med Res. 128(1):32–37.
   PubMed Web of Science ®Google Scholar
• Sparks R. 2012. Spatially clustered outbreak detection using the ewma scan statistics with multiple sized windows. Commun Stat Simul Comput. 41(9):1637–1653.
   Web of Science ®Google Scholar
• Sparks RS, Keighley T, Muscatello D. 2010. Early warning cusum plans for surveillance of negative binomial daily disease counts. J Appl Stat. 37(11):1911–1929.
   Web of Science ®Google Scholar
• Sparks RS, Keighley T, Muscatello D. 2011. Optimal exponentially weighted moving average (ewma) plans for detecting seasonal epidemics when faced with non-homogeneous negative binomial counts. J Appl Stat. 38(10):2165–2181.
   Web of Science ®Google Scholar
• Sun R, Tsung F. 2003. A kernel-distance-based multivariate control chart using support vector methods. Int J Prod Res. 41(13):2975–2989.
   Web of Science ®Google Scholar
• Tsui K, Chiu W, Gierlich P, Goldsman D, Liu X, Maschek T. 2008. A review of healthcare, public health, and syndromic surveillance. Qual Eng. 20(4):435–450.
   Google Scholar
• Tukey J. 1975. Mathematics and the picturing of data. In: Proceedings of the International Congress of Mathematicians, Vancouver, 1975. Vol. 2, p. 523–531.
   Google Scholar
• Unkel S, Farrington P, Garthwaite P, Robertson C, Andrews N. 2011. Statistical methods for the prospective detection of infectious disease outbreaks: a review. J R Stat Soc Series A (Stat Soc) 175(1):49–82.
   Web of Science ®Google Scholar
• Wand H, Ramjee G. 2010. Targeting the hotspots: investigating spatial and demographic variations in hiv infection in small communities in South Africa. J Int AIDS Soc. 13:41.
   PubMed Web of Science ®Google Scholar
• Weiß CH, Testik MC. 2019. Risk-based metrics for performance evaluation of control charts. Qual Reliab Eng Int. 35(1):280–291.
   Web of Science ®Google Scholar
• Wikipedia contributors. 2023. Abington township, montgomery county, pennsylvania; [accessed 2013 June 24]. https://en.wikipedia.org/w/index.php?title=Abington_Township,_Montgomery_County,_Pennsylvania&oldid=1161175928.
   Google Scholar
• Woodall W. 2006. The use of control charts in health-care and public-health surveillance. J Qual Technol. 38(2):89–104.
   Web of Science ®Google Scholar
• Woodall W, Montgomery D. 2014. Some current directions in the theory and application of statistical process monitoring. J Qual Technol. 46(1):78–94.
   Web of Science ®Google Scholar
• Xie X, Qiu P. 2023. Control charts for dynamic process monitoring with an application to air pollution surveillance. Ann Appl Stat 17(1):47–66.
   Web of Science ®Google Scholar
• Yan H, Paynabar K, Shi J. 2018. Real-time monitoring of high-dimensional functional data streams via spatio-temporal smooth sparse decomposition. Technometrics 60(2):181–197.
   Web of Science ®Google Scholar
• Yang K, Qiu P. 2020. Online sequential monitoring of spatio-temporal disease incidence rates. IISE Trans 52(11):1218–1233.
   Web of Science ®Google Scholar
• Zhang J, Li Z, Wang Z. 2010. A multivariate control chart for simultaneously monitoring process mean and variability. Comput Stat Data Anal 54(10):2244–2252.
   Web of Science ®Google Scholar
• Zhao Y, Zeng D, Herring AH, Ising A, Waller A, Richardson D, Kosorok MR. 2011. Detecting disease outbreaks using local spatiotemporal methods. Biometrics 67(4):1508–1517.
   PubMed Web of Science ®Google Scholar