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BEGIN:VEVENT
DESCRIPTION:You are invited to the Microsoft Africa Research Institute (MA
RI) Seminar\, where Professor Muthoni Masinde\, Head of CUT'\;s Depart
ment of Information Technology will present on the topic: A Prediction Mo
del for Malaria using an Ensemble of Machine Learning and Hydrological Dr
ought Indices.About the researchLiterature is awash with evidence that po
ints to climate change as one of the reasons for the upward increase in i
nfectious disease outbreaks around the globe. With the advancement in Art
ificial Intelligence (AI) and big climate data\, accurate analysis of med
ical and climate data allows prediction and early detection of infectious
diseases associated with climate change. The effectiveness of AI and dro
ught indices in predicting oncoming droughts has been demonstrated elsewh
ere in research\, this has however not been explored in the prediction of
infectious diseases. We present a model that uses machine learning to pr
edict the outbreak of infectious diseases using two drought indices and h
istorical incidents of malaria in Limpopo (South Africa). Having achieved
up to 99% prediction accuracies\, we demonstrate that such a model equip
s stakeholders with a new perspective when it comes to prediction of infe
ctious diseases&rsquo\; outbreaks that are associated with extreme climat
e variations.Attend the\nsession
DTEND:20220824T150000
DTSTAMP:20261025T172609Z
DTSTART:20220824T140000
SEQUENCE:0
SUMMARY:A Prediction Model for Malaria using an Ensemble of Machine Learni
ng and Hydrological Drought Indices
UID:ae341648-a1f2-486b-a924-73069c665999
X-ALT-DESC;FMTTYPE=text/html:
You are invited to th
e Microsoft Africa Research Institute (MARI) Seminar\, where Professor Mu
thoni Masinde\, Head of CUT'\;s Department of Information Technology w
ill present on the topic: A Prediction Model for Malaria using an
Ensemble of Machine Learning and Hydrological Drought Indices.<
/p>
About the research
Literature is awash with evi
dence that points to climate change as one of the reasons for the upward
increase in infectious disease outbreaks around the globe. With the advan
cement in Artificial Intelligence (AI) and big climate data\, accurate an
alysis of medical and climate data allows prediction and early detection
of infectious diseases associated with climate change. The effectiveness
of AI and drought indices in predicting oncoming droughts has been demons
trated elsewhere in research\, this has however not been explored in the
prediction of infectious diseases. We present a model that uses machine l
earning to predict the outbreak of infectious diseases using two drought
indices and historical incidents of malaria in Limpopo (South Africa). Ha
ving achieved up to 99% prediction accuracies\, we demonstrate that such
a model equips stakeholders with a new perspective when it comes to predi
ction of infectious diseases&rsquo\; outbreaks that are associated with e
xtreme climate variations.
Attend the\nsession
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