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\begin{document}

\title{Dakar to Douala}



\author[1]{George Busby}%
\affil[1]{University of Oxford}%


\vspace{-1em}



  
  \date{May 08, 2024}


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\section*{}

{\label{395592}}

Malaria is an infectious disease that accounts for some 300,000 deaths
every year in Africa. Whilst significant efforts over the last 20 years
mean that this number is around half of what it was in the year 2000, it
is still the biggest single disease killer across the breadth of Africa.
The main contributors to the reduced death toll have been the widespread
use of insecticide treated bednets, which stop infected mosquitoes from
biting people and therefore infecting them, and the better use of drugs
to treat disease. It is with renewed confidence then, that the global
community has articulated a grand plan to eradicate malaria by 2040.

However, there are at least two challenges to this ambitious proposal.
Just as we are starting to make real gains, the parasite is beginning to
fight back. The progress that has been made is in danger of being
reversed because the parasite is evolving resistance to our drugs and
this resistance is spreading. Therefore a crucial part of the global
strategy for malaria control is to monitor the spread of antimalarial
drug resistance, and identify and contain drug resistant strains when
they're found.

The second challenge relates to the changing nature of malaria
transmission. As parasite prevalence drops, fewer people are infected.
So malaria changes from being an endemic disease, where most people are
infected most of the time, to an epidemic disease where new infections
can arise in isolated groups of previously uninfected people. In such
cases, it is not clear whether these new infections represent the
recrudescence of local parasites or whether they are due to the
importation of new parasites from elsewhere. Local malaria control
strategies will differ greatly depending on which of these two scenarios
best explains new infections.

Fortunately, there is an approach that can provide both up-to-date
information about which drugs a parasite is resistant to and which
populations it is related to: DNA sequencing. Drug resistance manifests
itself as mutations in the parasite genome, and by comparing an unknown
parasite genome to a reference database, we can understand where it
comes from. Traditionally, genome sequencing has been expensive and
lab-based, and global parasite reference datasets have been unavailable.
But not any more.

C\selectlanguage{ngerman}ôte kwa pwani is a bold and innovative scientific expedition that will
take the very latest mobile genetic sequencing technology into remote
malarial regions of Africa and perform realtime genetic analysis of
parasite DNA in the field. For the first time, this will allow
scientists on the ground to test new malarial infections for the
presence of drug resistance and to understand where they have come from.
Côte kwa pwani is a collaboration between British and African scientists
and a major ambition for the expedition is to raise awareness for the
need for scientific training and infrastructure investment in Africa so
that future African scientists can perform similar analyses to those
undertaken during the expedition.

\section*{}

{\label{395592}}

\section*{Deaths from infectious diseases are disproportionately
concentrated in developing
countries}

{\label{395592}}

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is
a now annual analysis of the global, regional, and national trends in
life, death and disability~\cite{Murray_2017}. The GBD is a mammoth
effort involving thousands of researchers and provides an incredibly
useful window into the changing nature of life expectancy.
Encouragingly, the most recent incarnation shows that mortality rates
across all age groups have decreased over the last five years, meaning
that people are generally living longer, healthier lives than they have
done in the past. Despite this heartening news some 10.5 million deaths,
or~ just under 20\% of all globally were due to communicable, maternal,
and neonatal causes~\cite{Abajobir_2017}. Nevertheless, roughly 50\% fewer
people died from HIV in 2016 than in 2006, with numbers for TB dropping
by 20\% and malaria by 25\% over the same time period
\cite{Abajobir_2017}. Unfortunately, deaths from the most common
infectious diseases, like HIV, TB, and malaria disproportionately effect
those in the developing world.

\par\null

\section*{Data on health outcomes is worse in developing
countries}

{\label{948663}}\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/gr2-lrg/gr2-lrg}
\caption{{Classification of national time series of vital registration and verbal
autopsy data, 1980--2016, on the basis of the fraction of deaths well
certified and assigned to a detailed GBD cause
(from~\protect\cite{Abajobir_2017}). This maps shows that poor quality data on the
certification of death (0-2 stars) is concentrated in the developing
world.
{\label{438459}}%
}}
\end{center}
\end{figure}

\section*{}

{\label{177840}}

\section*{Building global data infrastructure to control infectious
disease in the developing
world}

{\label{177840}}

To achieve sustainable reduction of the large endemic burden of
infectious disease in the developing world, it is necessary to have
timely, relevant and actionable data - to understand what interventions
are likely to be effective, to target available resources for maximum
impact, to monitor whether or not interventions are having the desired
effect, and to alert control programmes when new problems emerge, e.g.
new strains of drug resistant or virulent pathogens.~ Recent
technological and scientific advances provide many opportunities to
tackle these problems, e.g. with mobile data collection, genomic
pathogen surveillance, cloud data platforms, novel statistical and
machine learning methodologies, sophisticated mathematical models for
decision support. The benefits of these recent advances are already
being realised in rich countries, e.g. in the National Health Service.

In many ways the need for such systems is greatest in the developing
world, because infectious diseases are so deeply entrenched, and because
the resources to tackle disease are limited, making it all the more
important that effective interventions are chosen and that resources are
allocated for maximum impact.~ However there are many practical
roadblocks to putting such information systems in place.~ There is lack
of local capacity, both for data generation and for data analysis.~
There is a lack of clarity about use cases for data, i.e. what are the
practical decisions that need to be made in disease control, and
precisely what data are needed to make these decisions?~ There is a need
for tools and infrastructure that are suitable for resource-poor
settings, e.g. for mobile data collection, pathogen genome sequencing,
data integration and analysis. Data are highly fragmented, both within
and between countries, and also between different infectious diseases.

\par\null\par\null\par\null

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