National Center for Vector Borne Disease Control · District Data
Malaria in India, a sixteen-year story
State- and district-level surveillance findings spanning burden, transmission intensity, reporting, resurgence risk and regional patterns.
Through 18 analytical questions, the following questions are answered: how malaria burden is changing, where residual risk is concentrated, and where the surveillance record needs closer scrutiny.
1 · Is decline broad-based, or concentrated in fewer districts?
How much of India’s malaria burden comes from the 10 districts reporting the most cases each year. If their share rises, the national decline may be happening mainly outside a smaller group of districts that continue to report many cases.
Fastest-declining districts
Plateauing / reversing districts
2 · Is the decline real, or a surveillance artifact?
Testing effort (ABER) compared with malaria incidence (API). If both fall together, some of the drop in reported cases may be because less testing was done, rather than because malaria itself fell.
3 · Is Pf displacing Pv as transmission drops?
How the share of P. falciparum cases has changed across India and in the higher-burden belt of Odisha, Chhattisgarh, Jharkhand, Madhya Pradesh and the Northeast. This helps us see whether the mix of malaria parasites is changing as overall cases fall.
4a · Has tribal-belt concentration grown or shrunk?
The combined share of India’s malaria cases reported by Odisha, Chhattisgarh, Jharkhand and Madhya Pradesh each year.
4b · Districts driving the tribal belt,
Which districts in Odisha, Chhattisgarh, Jharkhand and Madhya Pradesh report the most cases. Are cases falling across the whole belt, or are a few districts still accounting for much of the burden?
5 · Where is care/reporting failing relative to burden?
Reported deaths compared with malaria cases in each district, . Districts with many deaths relative to their number of cases may need a closer look at diagnosis, treatment and referral.
6 · Urban outliers vs rural high-burden districts
How malaria levels in large cities such as Mumbai, Chennai and Kolkata compare with higher-burden rural districts. The reasons behind malaria in cities can be quite different from those in rural areas, so the response may need to be different too.
7 · Testing intensity vs positivity — are we finding what's there?
Testing effort (ABER) compared with the share of tests that are positive (SPR), . The source reports do not provide ABER for individual districts, so this comparison is shown at the state level. States with less testing but a high share of positive tests may benefit from greater testing.
8 · Shock years: did COVID-era disruption show up in the trend?
How reported malaria cases changed from one year to the next around 2020–2022, using the same national case data shown above.
Findings · 9–18
9 · Population growth vs. case growth decoupling
Are malaria levels falling even in districts where the population is growing quickly? Points below the diagonal are districts where malaria cases grew more slowly than the population between 2015 and 2025.
10 · “Near-elimination” districts at risk of resurgence
Districts that had almost no reported malaria for two or three years and then saw cases rise again. These areas are worth watching, but the pattern alone cannot tell us why cases increased.
11 · Border / cross-state spillover
Do malaria patterns look similar across state borders? A state-level view of the Chhattisgarh–Odisha–Jharkhand–MP corridor is included. The data do not include district boundary maps, so this gives us a broad picture rather than showing which neighbouring districts are directly connected.
12 · Rate of decline vs. starting burden
How much malaria fell in each district between 2015 and 2025 compared with the district’s starting level in 2015. This helps us see whether districts that started with more malaria have been making slower or faster progress.
13 · Death reporting consistency
Districts that reported many malaria cases but no deaths for several years in a row. These patterns are worth a closer look to understand how consistently deaths were being reported.
14 · Year-over-year volatility
Which districts show large ups and downs in reported cases rather than a steady decline? A higher score means bigger changes from one year to the next.
15 · Pf% trajectory as an early-warning signal
Does a rise in the share of P. falciparum cases in one year followed by more malaria cases the next year? The patterns shown here are signals worth looking at more closely, not proof that one caused the other.
16 · Regional classification sensitivity
Would the overall picture of Central India change if Uttar Pradesh were counted as part of Central India, or Madhya Pradesh were counted as North India? This comparison is done at the state level; a district-level comparison would need district boundary maps.
17 · Is P. vivax rising in absolute terms?
National P. vivax case counts by year. Pv cases fell sharply through the early 2010s alongside the overall decline, but what happened after?.
18 · P. vivax share of total cases
P. vivax as a percentage of all confirmed cases, nationally.
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