CHICAS research is 10th anniversary highlight

CHICAS at Lancaster Medical School

The Centre for Health Informatics, Computing and Statistics at Lancaster Medical School (CHICAS) has been singled out for praise by the international Coalition for Operational Research on Neglected Tropical Diseases (COR-NTD).

Celebrating its tenth anniversary, COR-NTD has been supporting global endeavours to achieve the World Health Organization's targets for neglected tropical disease (NTD) control and elimination.

CHICAS is a designated WHO Collaborating Centre whose contributions have been recognized as one of the program's ten highlights, spotlighted during the opening plenary session at this year's COR-NTD meeting in Chicago, symbolizing a decade of impactful NTD research and collaboration.

Emeritus Professor Peter Diggle of CHICAS said: “The COR-NTD meeting is the place where researchers and policy makers working on the control and elimination of Neglected Tropical Diseases meet each year to share ideas and plan future work. At this year’s meeting in Chicago, it was very gratifying, and somewhat humbling, to see CHICAS’s statistical work recognised as one of ten highlights of COR-NTD’s first decade.”

The Head of CHICAS Dr Emanuele Giorgi said: “This recognition from one of the most prominent NTD public health organizations underscores the hard work of CHICAS over the years. This achievement, coupled with CHICAS being designated as a WHO Collaborating Centre in 2020, highlights the substantial influence our research on the application of geospatial methods for NTD control and elimination has had on shaping policy decisions."

Established in 2013, COR-NTD serves as a multi-donor initiative, facilitating collaborative research projects with implementing partners in some of the world's most impoverished regions, where NTDs are prevalent. These projects aim to address critical health challenges and enhance the well-being of affected populations.

Among COR-NTD's supported initiatives, the work of CHICAS in the development and application of geospatial statistical methods for global population health research stands out.

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