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CPS1952 Michele N. et al.
                 differential equation approach’, Journal of the Royal Statistical Society:
                 Series B (Statistical Methodology) 73(4), 423-498.
            6.  Mabaso, M. L. H., Craig, M., Vounatsou, P. & Smith, T. (2005), ‘Towards
                 empirical  description  of  malaria  seasonality  in  southern  Africa:  the
                 example of Zimbabwe’, Tropical Medicine & International Health 10(9),
                 909-918.
            7.  Martinez,  M.  E.  (2018),  ‘The  calendar  of  epidemics:  seasonal  cycles  of
                 infectious diseases’, PLoS Pathogens 14(11), e1007327.
            8.  Pewsey, A., Neuhäuser, M. & Ruxton, G.D. (2013), Circular Statistics in R,
                 Oxford University Press.
            9.  Stuckey,  E.  M.,  Smith,  T.  &  Chitnis,  N.  (2014),  ‘Seasonally  dependent
                 relationships  between  indicators  of  malaria  transmission  and  disease
                 provided  by  mathematical  model  simulations’,  PLoS  Computational
                 Biology 10(9), e1003812.
            10.  Valle, D. & Lima, J. M. T. (2014), ‘Large-scale drivers of malaria and priority
                 areas for prevention and control in the Brazilian Amazon region using a
                 novel multi-pathogen geospatial model’, Malaria Journal 13(1), 443.
            11.  World Health Organization (2018), World Malaria Report 2018, Geneva.










































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