Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.
The authors used a supervised machine learning approach to detect potential disease reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence.
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References
1.
LopezAD, MathersCD, EzzatiM, JamisonDT, MurrayCJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet, 2006; 367(9524):1747-1757.
2.
TeixeiraMG, Costa M daC, BarretoF, BarretoML. Dengue: twenty-five years since reemergence in Brazil. Cad Saúde Pública, 2009; 25(Suppl 1):S7-S18.
3.
AntonaD, Lévy-BruhlD, BaudonC, et al.Measles elimination efforts and 2008-2011 outbreak, France. Emerg Infect Dis, 2013; 19(3):357-364.
4.
WoodallJP, YuillTM. Why is the yellow fever outbreak in Angola a ‘threat to the entire world’?. Int J Infect Dis, 2016; 48:96-97.
5.
PlansP, TornerN, GodoyP, JanéM. Lack of herd immunity against measles in individuals aged <35 years could explain reemergence of measles in Catalonia (Spain). Int J Infect Dis, 2014; 18:81-83.
6.
SandersJW, FuhrerGS, JohnsonMD, RiddleMS. The epidemiological transition: the current status of infectious diseases in the developed world versus the developing world. Sci Prog, 2008; 91(Pt 1):1-37.
7.
OmranAR. The epidemiologic transition: a theory of the epidemiology of population change. 1971. Milbank Q, 2005; 83(4):731-757.
8.
MorseSS. Factors in the emergence of infectious diseases. Emerg Infect Dis, 1995; 1(1):7-15.
9.
Understanding emerging and re-emerging infectious diseases. In: National Institutes of Health. Biological Sciences Curriculum Study NIH Curriculum Supplement Series. National Institutes of Health; 2007. https://www.ncbi.nlm.nih.gov/books/NBK20364/.
10.
MorensDM, FauciAS. Emerging infectious diseases: threats to human health and global stability. PLoS Pathog, 2013; 9(7):e1003467.
11.
BarrettR, KuzawaCW, McDadeT, ArmelagosGJ. Emerging and re-emerging infectious disease: the third epidemiologic transition. Annu Rev Anthropol, 1998; 27(1):247-271.
12.
PolandGA, JacobsonRM. The reemergence of measles in developed countries: time to develop the next-generation measles vaccines?. Vaccine, 2012; 30(2):103-104.
13.
FreymuthF, VabretA. Measles, a re-emerging disease in France?. Clin Microbiol Infect, 2011; 17(6):793.
BiellikR. First 5 years of measles elimination in southern Africa: 1996-2000. Lancet, 2002; 359:1564-1568.
16.
GroutL, MinettiA, HurtadoN, et al.Measles in Democratic Republic of Congo: an outbreak description from Katanga, 2010-2011. BMC Infect Dis, 2013; 13(1):232.
17.
ManciniS. Description of a large measles epidemic in Democratic Republic of Congo, 2010-2013. Confl Health, 2014; 8:9.
18.
GerardSP, KyrousisE, ZachariahR. Measles in the Democratic Republic of Congo: an urgent wake-up call to adapt vaccination implementation strategies. Public Health Action, 2014; 4:6-8.
LeiteRD, BarretoJL, SousaAQ. Measles reemergence in Ceará, Northeast Brazil, 15 years after elimination. Emerg Infect Dis, 2015; 21(9):1681-1683.
22.
WangX, BoultonML, MontgomeryJP, et al.The epidemiology of measles in Tianjin, China, 2005-2014. Vaccine, 2015; 33:6186-6191.
23.
VatevNT, StoychevaMV, PetrovAI. Reemergence of measles in Bulgaria: a large outbreak in Plovdiv, 2010. Braz J Infect Dis, 2011; 15:613-616.
24.
ParkY-J, EomHS, KimES, ChoeYJ, BaeG-R, LeeD-H. Reemergence of measles in South Korea: implications for immunization and surveillance programs. Jpn J Infect Dis, 2013; 66(1):6-10.
25.
AndersonRM. The concept of herd immunity and the design of community-based immunization programmes. Vaccine, 1992; 10(13):928-935.
26.
KremerJR. Measles virus strain diversity, Nigeria and Democratic Republic of the Congo. Emerg Infect Dis, 2010; 16:1724-1730.
27.
FairchildG, TasseffB, KhalsaH, et al.Epidemiological data challenges: planning for a more robust future through data standards. Front Public Health, 2018; 6:336.
28.
HeywoodAE.Measles: a re-emerging problem in migrants and travellers. J Travel Med, 2018; 25(1).
29.
KukretiH, MittalV, ChaudharyA, et al.Continued persistence of a single genotype of dengue virus type-3 (DENV-3) in Delhi, India since its reemergence over the last decade. J Microbiol Immunol Infect, 2010; 43(1):53-61.
30.
MasuhH. Reemergence of dengue in Argentina: historical development and future challenges. WHO Regional Office for South-East Asia; 2008. http://www.who.int/iris/handle/10665/170467. Accessed July15, 2019.
31.
HeringerM, NogueiraRMR, de FilippisAMB, et al.Impact of the emergence and reemergence of different dengue viruses' serotypes in Rio de Janeiro, Brazil, 2010 to 2012. Trans R Soc Trop Med Hyg, 2015; 109(4):268-274.
32.
DomingosP. A few useful things to know about machine learning. Commun ACM, 2012; 55(10):78.
33.
ZhouL, PanS, WangJ, VasilakosAV. Machine learning on big data: opportunities and challenges. Neurocomputing, 2017; 237:350-361.
34.
LamposV, De BieT, CristianiniN. Flu detector—tracking epidemics on Twitter. In: BalcázarJL, BonchiF, GionisA, SebagM, eds. Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg: Springer Berlin Heidelberg;. 2010:599-602.
35.
JensenPB, JensenLJ, BrunakS. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet, 2012; 13(6):395-405.
36.
SalathéM, KhandelwalS. Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLoS Comput Biol, 2011; 7(10):e1002199.
World Health Organization. Dengue Net—WHO's Internet-based system for global surveillance of dengue fever and dengue haemorrhagic fever. WHO Regional Office for South-East Asia; 2002. http://apps.who.int/iris/handle/10665/163773. Accessed July15, 2019.
PedregosaF, VaroquauxG, GramfortA, et al.Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 2011; 12:2825-2830.
44.
AliJ, KhanR, AhmadN, MaqsoodI. Random forests and decision trees. International Journal of Computer Science, 2012; 9.
45.
TranHD, AlamM, TrungNV, et al.Multi-drug resistant Vibrio cholerae O1 variant El Tor isolated in northern Vietnam between 2007 and 2010. J Med Microbiol, 2012; 61(Pt 3):431-437.
46.
BompangueD, VesenbeckhSM, GiraudouxP, et al.Cholera ante portas—the re-emergence of cholera in Kinshasa after a ten-year hiatus. PLoS Curr, 2012; 4:RRN1310.
47.
ValiaR, TaviniE, SpagnolettiM, CeccarelliD, CappuccinelliP, ColomboMM. Vibrio cholerae O1 epidemic variants in Angola: a retrospective study between 1992 and 2006. Front Microbiol, 2013; 4:3554.
48.
GrobbelaarAA, WeyerJ, MoollaN, Jansen van VurenP, MoisesF, PaweskaJT. Resurgence of yellow fever in Angola, 2015-2016. Emerg Infect Dis, 2016; 22(10):1854-1855.
49.
SowA, BaY, DialloD, et al.Re-emergence of yellow fever in Kedougou, Southeastern Senegal in 2010-2011. Int J Infect Dis, 2014; 21:219.
FunkS, BogichTL, JonesKE, KilpatrickAM, DaszakP. Quantifying trends in disease impact to produce a consistent and reproducible definition of an emerging infectious disease. PLoS One, 2013; 8(8):e69951.
52.
SnowR, O'ConnorB, JurafskyD, NgAY. Cheap and fast—but is it good?: evaluating non-expert annotations for natural language tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing; Association for Computational Linguistics; 2008:254-263.
53.
ChristakiE. New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence, 2015; 6(6):558-565.
54.
Measles and rubella monitoring May 2013. Stockholm; European Centre for Disease Prevention and Control; 2013 May.
55.
AsariaP, MacMahonE. Measles in the United Kingdom: can we eradicate it by 2010?. BMJ, 2006; 333:890-895.
56.
MurakamiH, Van CuongN, Van TuanH, TsukamotoK, HienDS. Epidemiological impact of a nationwide measles immunization campaign in Viet Nam: a critical review. Bull World Health Organ, 2008; 86:948-955.
57.
NmorJC, ThanhHT, GotoK. Recurring measles epidemic in Vietnam 2005-2009: implication for strengthened control strategies. Int J Biol Sci, 2011; 7:138-146.
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