Dual infection with HIV and malaria fuels the spread of both diseases in sub-Saharan Africa.
ABSTRACT Mounting evidence has revealed pathological interactions between HIV and malaria in dually infected patients, but the public health implications of the interplay have remained unclear. A transient almost one-log elevation in HIV viral load occurs during febrile malaria episodes; in addition, susceptibility to malaria is enhanced in HIV-infected patients. A mathematical model applied to a setting in Kenya with an adult population of roughly 200,000 estimated that, since 1980, the disease interaction may have been responsible for 8,500 excess HIV infections and 980,000 excess malaria episodes. Co-infection might also have facilitated the geographic expansion of malaria in areas where HIV prevalence is high. Hence, transient and repeated increases in HIV viral load resulting from recurrent co-infection with malaria may be an important factor in promoting the spread of HIV in sub-Saharan Africa.
- SourceAvailable from: Alessandro Vespignani[show abstract] [hide abstract]
ABSTRACT: Interactions among multiple infectious agents are increasingly recognized as a fundamental issue in the understanding of key questions in public health regarding pathogen emergence, maintenance, and evolution. The full description of host-multipathogen systems is, however, challenged by the multiplicity of factors affecting the interaction dynamics and the resulting competition that may occur at different scales, from the within-host scale to the spatial structure and mobility of the host population. Here we study the dynamics of two competing pathogens in a structured host population and assess the impact of the mobility pattern of hosts on the pathogen competition. We model the spatial structure of the host population in terms of a metapopulation network and focus on two strains imported locally in the system and having the same transmission potential but different infectious periods. We find different scenarios leading to competitive success of either one of the strain or to the codominance of both strains in the system. The dominance of the strain characterized by the shorter or longer infectious period depends exclusively on the structure of the population and on the the mobility of hosts across patches. The proposed modeling framework allows the integration of other relevant epidemiological, environmental and demographic factors, opening the path to further mathematical and computational studies of the dynamics of multipathogen systems.PLoS Computational Biology 08/2013; 9(8):e1003169. · 4.87 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Infectious livestock diseases remain a major threat to attaining food security and are a source of economic and livelihood losses for people dependent on livestock for their livelihood. Knowledge of the vital infectious diseases that account for the majority of deaths is crucial in determining disease control strategies and in the allocation of limited funds available for disease control. Here we have estimated the mortality rates in zebu cattle raised in a smallholder mixed farming system during their first year of life, identified the periods of increased risk of death and the risk factors for calf mortality, and through analysis of post-mortem data, determined the aetiologies of calf mortality in this population. A longitudinal cohort study of 548 zebu cattle was conducted between 2007 and 2010. Each calf was followed during its first year of life or until lost from the study. Calves were randomly selected from 20 sub-locations and recruited within a week of birth from different farms over a 45 km radius area centered on Busia in the Western part of Kenya. The data comprised of 481.1 calf years of observation. Clinical examinations, sample collection and analysis were carried out at 5 week intervals, from birth until one year old. Cox proportional hazard models with frailty terms were used for the statistical analysis of risk factors. A standardized post-mortem examination was conducted on all animals that died during the study and appropriate samples collected. The all-cause mortality rate was estimated at 16.1 (13.0-19.2; 95% CI) per 100 calf years at risk. The Cox models identified high infection intensity with Theileria spp., the most lethal of which causes East Coast Fever disease, infection with Trypanosome spp., and helminth infections as measured by Strongyle spp. eggs per gram of faeces as the three important infections statistically associated with infectious disease mortality in these calves. Analysis of post-mortem data identified East Coast Fever as the main cause of death accounting for 40% of all deaths, haemonchosis 12% and heartwater disease 7%. The findings demonstrate the impact of endemic parasitic diseases in indigenous animals expected to be well adapted against disease pressures. Additionally, agreement between results of Cox models using data from simple diagnostic procedures and results from post-mortem analysis underline the potential use such diagnostic data to reduce calf mortality. The control strategies for the identified infectious diseases have been discussed.BMC Veterinary Research 09/2013; 9(1):175. · 1.86 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: This study assessed the effects of Hexamita salmonis (Moore) on metabolism of rainbow trout Oncorhynchus mykiss (Walbaum) and its effect on the host's susceptibility to infectious pancreatic necrosis virus (IPNV) after antiparasitic treatment. Rainbow trout naturally infected with H. salmonis were treated with 10 mg metronidazole kg fish(-1) per day, and their physiological recovery was assessed through measuring resting metabolism on the 7th, 14th, 21st and 28th day after treatment. In addition, we exposed the naïve fish to H. salmonis and measured the resting metabolism (oxygen consumption as mg O2 kg(-1) per hour) on the 10th, 20th and 30th day after the exposure to assess the variation in metabolic rates after infection. Significantly lower rates of metabolic activity (P < 0.05) were anticipated 20 days after infection with H. salmonis compared with the fish infected with H. salmonis for 10 days or with the parasite-free fish. Similarly, the treated fish needed about 20 days to fully recover from hexamitiasis. The susceptibility of rainbow trout to IPNV remained unchanged in the presence of H. salmonis. Weight loss was significantly higher (P < 0.05) in infected than that in the parasite-free fish. Fish should be examined regularly for H. salmonis and treated immediately whether found to prevent economic losses and excessive size variation.Journal of Fish Diseases 10/2013; · 1.59 Impact Factor
, 1603 (2006);
et al. Laith J. Abu-Raddad,
of Both Diseases in Sub-Saharan Africa
Dual Infection with HIV and Malaria Fuels the Spread
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pRNA, which suggests that 6S RNA can be
used as a template for transcription in extract.
Extract containing samples required heparin treat-
ment to resolve specific complexes on native
gels; therefore, 6S RNA–pRNA:core complexes
were not observed. Gradient fractionation of a
stationary phase extract after incubation with
NTPs similarly demonstrated significant release
of 6S RNA from Es70(fig. S5).
The 6S RNA:Es70complex is stable in vitro
and accumulates to high levels in stationary
phase, raising the question of how 6S RNA–
RNAP interactions are disrupted when cells re-
initiate growth. To test if RNA synthesis could
mediate timely 6S RNA release, endogenous 6S
RNA complexes were examined in extracts pre-
pared from cells after dilution into rich medium
(Fig. 3C). A 6S RNA complex with mobility
suggesting it was 6S RNA–pRNA was detected
in extracts from cells 2 to 30 min after dilution.
Extracts were not incubated nor NTPs added;
therefore, complexes represent those formed in
vivo. Addition of an RNAP inhibitor (rifampi-
cin) to the dilution medium prevented 6S RNA–
pRNA complex formation, which demonstrated
that its formation requires RNA synthesis. The
presence of 6S RNA–pRNA complexes in cells
after outgrowth signifies that Es70uses 6S RNA
as a template for RNA synthesis at this time,
consistent with the hypothesis that 6S RNA
release from Es70could be mediated through
We propose that 6S RNA–templated RNA
synthesis occurs in response to the rapid in-
crease in NTP pools upon outgrowth (26). In
vitro RNA synthesis from 6S RNA required
higher concentrations of NTPs (>50 mM) than
several tested DNA promoters (<1 mM NTPs)
(fig. S6), which suggests that transcription from
6S RNA is more sensitive to NTP concentration.
Other factors also may affect the relative sta-
RNA synthesis from 6S RNA, as the 6S RNA–
pRNA complex was observed only early after
exit from stationary phase and did not persist
through exponential growth.
In addition to freeing RNAP from 6S RNA
inhibition, the RNA synthesis reaction likely
results in decreased stability of 6S RNA. 6S
RNA levels are decreased during outgrowth
(fig. S7) and do not reach maximum levels
until well after transition into stationary phase
(7). Such decreased stability might be due to
increased accessibility of 6S RNA to cellular
nucleases on release from RNAP or could be
through direct recognition of the 6S RNA–
pRNA duplex. It also is tempting to consider
whether the pRNA has a cellular function
distinct from the role its synthesis has in 6S
RNA release, especially because the template
region within 6S RNA is more conserved than
the rest of the RNA (8).
Control of 6S RNA levels and 6S RNA–
Es70interactions in direct response to NTP con-
centration create a regulatory circuit where
release from RNAP and control of stability of
the sRNA inhibitor depend on the same features
of the RNA required for its inhibitory nature.
Precise positioning of 6S RNA in the active site
of RNAP blocks DNA promoter binding but
allows synthesis-mediated release of 6S RNA.
The mechanism of 6S RNA inhibition appears
to differ from FC* and B2 RNA, which do not
exclude DNA binding within preinitiation com-
plexes (4), which suggests their mechanism of
release also will be distinct. However, a com-
mon theme for sRNA inhibitors of RNAP may
be to exploit inherent properties and activities of
the enzyme for its inhibition, as well as for its
release from such regulation.
References and Notes
1. G. Storz, S. Altuvia, K. M. Wassarman, Annu. Rev.
Biochem. 74, 199 (2005).
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Biol. 12, 313 (2005).
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Nature 439, 617 (2006).
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J. Mol. Biol. 302, 1049 (2000).
26. H. D. Murray, D. A. Schneider, R. L. Gourse, Mol. Cell 12,
27. We thank R. Landick for RNAP, A. Klocko for His-s70,
I. Toulokhonov for Fe-cleavage protocols, and S. P.
Haugen for pRLG-7610; R. Landick, R. Gourse, G. Storz,
T. Record, C. Bingman, C. A. Davis and members of the
Wassarman laboratory for many helpful discussions and
comments on the manuscript. This research was supported
by the NIH GM67955 (K.M.W.) and GM23467 (R.M.S.).
Supporting Online Material
Materials and Methods
Figs. S1 to S7
7 September 2006; accepted 1 November 2006
Dual Infection with HIV and Malaria
Fuels the Spread of Both Diseases
in Sub-Saharan Africa
Laith J. Abu-Raddad,1,2* Padmaja Patnaik,3James G. Kublin4,5*
Mounting evidence has revealed pathological interactions between HIV and malaria in dually
infected patients, but the public health implications of the interplay have remained unclear. A
transient almost one-log elevation in HIV viral load occurs during febrile malaria episodes; in
addition, susceptibility to malaria is enhanced in HIV-infected patients. A mathematical model
applied to a setting in Kenya with an adult population of roughly 200,000 estimated that, since
1980, the disease interaction may have been responsible for 8,500 excess HIV infections and
980,000 excess malaria episodes. Co-infection might also have facilitated the geographic
expansion of malaria in areas where HIV prevalence is high. Hence, transient and repeated
increases in HIV viral load resulting from recurrent co-infection with malaria may be an important
factor in promoting the spread of HIV in sub-Saharan Africa.
as over 500 million clinical Plasmodium
falciparum infections occur every year with
more than a million malaria-associated deaths
(2). There is considerable geographic overlap
between the two diseases, particularly in sub-
n Africa, an estimated 40 million people
are infected with HIV, resulting in an an-
nual mortality of over 3 million (1), where-
Saharan Africa (3), and growing evidence of
an interactive pathology (4–10). HIV has been
shown to increase the risk of malaria infection
greatest impact in immune-suppressed persons
(4, 6, 8–10). Conversely, malaria has been
shown to induce HIV-1 replication in vitro (11)
and in vivo (5, 7). A biological explanation for
VOL 3148 DECEMBER 2006
CORRECTED 2 FEBRUARY 2007; SEE LAST PAGE
on April 25, 2010
these interactions lies in the cellular-based
immune responses to HIVand malaria (11–13).
There is a functional relationship between
HIV-1 plasma viral load and transmission
probability per coital act, in which a logarith-
mic increase in viral load is associated with a
2.45-fold increase in transmission probability
ability per stage of infection indicate that the
acute stage of HIV infection, during which
the viral load peaks at a two-log excess over the
chronic stage, plays a pivotal role in transmis-
sion (15, 16). This amplification seems to be re-
least in the early stages of the epidemic (16, 17).
A prospective study of dual infection with
HIV and malaria has confirmed and extended
earlier findings (4–6, 8–10) that, first, co-
infection leads to a near one-log increaseinviral
load in chronic-stage HIV-infected patients
during febrile malaria episodes (7) and, second,
HIV infection substantially increases suscepti-
bility to malaria infection (9). These findings
have highlighted the need for a robust quantita-
tive assessment of the population-level implica-
tions of the immune-mediated interaction of the
two diseases (18).
Thus, we asked the question: does recurrent
malaria promote HIV transmission because of a
concomitant elevation of viremia during febrile
periods? In the absence of field studies that
directly measure the effect of malaria on HIV
spread, we attempted to answer this question by
synthesizing recent quantitative biological find-
ings into a mathematical model that estimates
the impact of HIV and malaria on one another
(19). The core assumptions of our model are
shown in Table 1. The duration of the height-
ened viral load and the impact of co-infection
on sexual activity are not adequately charac-
terized parameters. The supporting online ma-
terial details the bases of our parameter choices
and quantifies the impact of the uncertainty in
the assumed parameters by means of univariate
and multivariate sensitivity analyses (19). These
analyses indicate a significant role for dual
infection in fueling the spread of both diseases
in sub-Saharan Africa
We examined the impact of the synergy in
Kisumu, Kenya, a setting with high HIV and
malaria prevalences. Malaria prevalence refers
here to any malaria parasitaemia rather than to
clinical disease alone. In the presence of in-
teraction between the two diseases, the HIV
epidemic peak is 8% higher whereas the ma-
laria peak is 13% larger than the levels in a
scenario where there is no interaction (Fig. 1).
The excess prevalence, which is the baseline
prevalence subtracted from the prevalence
after the inclusion of the interaction, is 2.1%
for HIVand 5.1% for malaria, respectively. In
the Kisumu district [with an adult human
population ≈ 200,000 (19)], the interaction in
the absence of malaria intervention may ac-
count for a cumulative 8,500 excess HIV in-
fections and 980,000 excess malaria episodes
since 1980. Furthermore, for the period from
1990 through 2005, a duration marked by an
1Statistical Center for HIV/AIDS Research and Prevention, Fred
Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
2Center for Studies in Demography and Ecology, University
of Washington, Seattle, WA 98195, USA.
Epidemiology, School of Public Health, University of North
Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
4International Health Program, University of Washington,
Seattle, WA 98195, USA.5Clinical Research Division, Fred
Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
*To whom correspondence should be addressed. E-mail:
firstname.lastname@example.org (L.J.A.); email@example.com (J.G.K.)
Table 1. The core assumptions of our HIV/malaria interaction model.
Rate ratio increase in HIV coital transmission
probability per one-log (base 10) rise in viral load
Logarithmic increase in HIV viral load level during
Chronic stage with clinical malaria
Chronic stage with nonclinical malaria
Susceptibility enhancement to malaria infection
in HIV-infected persons
Duration of heightened viral load during
Fractional reduction in sexual activity
during malarial infection
Fraction of malaria-infected patients
developing clinical malaria
Enhanced HIV mortality in dually infected patients
Areas of stable malaria
Areas of nonstable malaria
(5, 7, 19)
(19, 25, 26)
(4, 10, 27)
(19, 28, 29)
Fig. 1. The time course of HIV and malaria interaction in Kisumu, Kenya. HIV and malaria
prevalences in Kisumu as compared with the baseline predictions in the absence of interaction are
shown. The measured prevalences were extracted from several studies (19).
8 DECEMBER 2006VOL 314
on April 25, 2010
average HIV prevalence of roughly 25%, the
fraction of HIV infections attributable to
malaria is 4.8% whereas that of malaria
promoted by HIV is 9.9%. The latter estimate
accords well with a derived estimate from
rural Uganda (10). We estimate that an HIV
prevalence that reached 24% in 1995 would
have needed two additional years to reach this
level in the absence of synergy with malaria.
We proceed to describe the interaction in
diverse settings with different HIVand malaria
prevalence levels. We characterized the syner-
gy at the endemic equilibrium of both diseases
and used the average sexual partner acquisition
rate (ravg) in the population as a proxy for HIV
baseline prevalence level and Macdonald’s
stability index (MSI) (20) as a proxy for that
of malaria (19). Once we incorporated the in-
teraction between the two diseases in the di-
verse settings described in Fig. 2, A and B, we
derived the excess prevalences (Fig. 2, C and
D). It is evident how the interplay, though
dependent on baseline measures, can consid-
erably increase HIV and malaria prevalences.
The largest increase occurs when one baseline
measure is very high while the other is very
low and near its endemic threshold. For ex-
ample, a setting with 1.0% malaria but 37.8%
HIV at baseline prevalence transforms into a
setting of 9.2% malaria and a barely changed
value of 38.5% HIV. When both prevalences
are very high, the impact of the interaction is
minimal. For HIV, there are two “endemic
thresholds” arising for each of the two sexual
risk groups assumed in our model. The first
threshold is when sexual transmission becomes
sustainable in the high-risk group, whereas the
other threshold is when the transmission
becomes sustainable in the general population
(low-risk group) once the partner change rate is
high enough to support sustainable transmis-
sion, even in the absence of mixing with the
Furthermore, if one of the diseases is at
endemic equilibrium while the other is just
below its threshold, the interaction can lower
the threshold of the second disease, thereby
allowing this disease to reach endemic stabil-
ity. This effect can be seen in Fig. 3, where the
interaction has lowered the endemicity thresh-
old for malaria from MSI ¼ 1:353 to 1:270 (a
6% reduction). A myriad of factors, however,
affect malaria ecology, so lowering the thresh-
old does not necessarily expand the distribution
of malaria. Nevertheless, in areas that can sup-
port malaria with a small change in the en-
tomological or transmission parameters, the
interaction can drive unstable malaria preva-
lence toward stability. Though not evident in the
figure because of the small absolute change, the
interaction has also lowered the HIVendemicity
threshold (the threshold of sustainability in the
high-risk group) by 6% from ravg¼ 0:456 to
ravg¼ 0:430 partners per year (corresponding
to rhigh?risk¼ 2:261 to rhigh?risk¼ 2:132 part-
ners per year).
The rapid increase in excess prevalence in
Figs. 2, C and D, and 3 just above the threshold
implies that settings with high HIV (or malaria)
endemicity but with low or unstable malaria (or
HIV) prevalence are particularly at risk for this
interaction. Given that, in areas of unstable
malaria endemicity, a larger part of the malaria
burden is in adults in whom HIV is concen-
trated, the high HIV prevalence, for example in
Fig. 2. Excess HIV and malaria prevalences in a wide range of settings. The equilibrium prev-
alences of HIV (A) and malaria (B) in the absence of interaction are shown as functions of ravgand
MSI. Both parameters increase geometrically to capture a wide spectrum corresponding to a change
in baseline adult HIV prevalence from 0 to 50% and baseline adult malaria prevalence from 0 to
70%. (C) and (D) display the corresponding excess HIV and malaria prevalences. Excess prevalence
is defined as no-interaction prevalence subtracted from the prevalence in the presence of inter-
action. Colored gradients correspond to the units in the Y axis.
Fig. 3. Interaction impact on
shifting endemicity thresholds. (A)
HIV prevalence in the absence of
interaction, in its presence, and in
excess prevalence as a function of
ravg in a setting of 30% malaria
baseline prevalence. (B) Malaria
prevalence in the absence of inter-
prevalence as a function of MSI in a
setting of 25% HIV baseline preva-
lence. Excess prevalence is a mani-
curves for each of the diseases to
below threshold after interaction.
VOL 314 8 DECEMBER 2006
on April 25, 2010
South Africa, can intensify and possibly sta-
bilize malaria endemicity.
Korenromp et al. have assessed the impact
of HIV on malaria in sub-Saharan Africa and
indicated that the overall impact is limited
because of differences in geographic distribu-
tions and age patterns between the two dis-
eases, although the effect in the presence of
geographic overlap can be locally considera-
ble and is substantial in areas of high HIV
with unstable malaria as we predict (21). In
some parts of Africa, the geographic overlap
may increase if HIV continues to spread from
urban centers to rural areas. Our analysis indi-
cates that the impact on malaria is at its maxi-
mum when the number of advanced HIV cases
reaches its zenith shortly after the HIVepidem-
ic peaks (Fig. 1), a trajectory akin to that of
tuberculosis (22). Nonetheless, the malaria
peak lags behind that of HIVat most by 1 year,
in contrast to that of tuberculosis, which lags
by 7 years (23).
Our model can be expanded to accommo-
date general intervention measures such as
provision of condoms and insecticide-treated
bednets, but here we have focused on mea-
sures that target the interaction in co-infected
persons. Thus, we have specifically modeled
the effect of malaria treatment of HIV-infected
patients, assuming either that such treatment
shortens the period of heightened HIV viral load
or that prophylaxis prevents malaria infection
from being established in HIV-infected patients
in the first place (8). We varied the malaria in-
fectious period (gametocytaemia) from 0 to 60
days in HIV-infected patients (Fig. 4A) and ob-
served a steady decline in excess HIV prev-
alence as we cut back the duration of malaria
episode. However, the outcome showed that
malaria treatment is more effective in reducing
malaria prevalence than it is at reducing the
prevalence of HIV. Shortening gametocytaemia
to less than 27 days eliminates all HIV-induced
We also tested the impact of a loss of sex-
ual activity during malaria episodes among
clinical malaria–infected patients (Fig. 4B).
The impact on HIV is considerable, but it is
minimal on malaria. A 36% reduction in ac-
tivity can remove all excess HIV prevalence.
Avoidance of sex during, and for 8 weeks after,
malarial fever would considerably diminish
HIV spread, but this degree of intervention is
probably impractical to implement despite key
successes in behavioral interventions such as in
Uganda (24). A more-effective approach may be
an emphasis on treatment of malaria and pro-
tection against mosquitoes for HIV-infected
persons. Thus, linking health services for HIV
and malaria would be advantageous. The combi-
nation of cotrimoxazole prophylaxis, antiret-
roviral therapy, and insecticide-impregnated
bednets can reduce the incidence of malaria by
95% in HIV-infected persons (8).
Our model shows that transient but repeated
elevated HIV viral loads associated with recur-
rent co-infections, such as malaria, can amplify
HIVprevalence.Thisfinding suggestsone more
independent explanatory variable for the high
HIVincidence and rapid spread of HIVinfection
in sub-Saharan Africa. Diseases that are not
sexually transmitted can thus affect the natural
spread. Our work highlights the need for field
studies that better characterize the parameters of
the interaction and explore the impact of inter-
vention measures. However, such studies must
account for the ethical considerations posed by
the recent findings of Mermin et al. (8) that there
are effective interventionsto reduce theincidence
of malaria in HIV-infected persons. Finally, we
emphasize the need for more-concerted health
services for early and effective treatment and
prevention of malaria in HIV-infected persons.
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Supporting Online Material
Materials and Methods
Figs. S1 and S2
Tables S1 to S6
11 July 2006; accepted 1 November 2006
Fig. 4. Impact of potential inter-
ventions and the sensitivity of
predictions to key assumptions
about the parameters of the inter-
action. (A) Impact of malaria
treatment on dually infected
patients as expressed in HIV and
malaria prevalences in the pres-
ence of interaction and treatment
as compared with the baseline
with no interaction and no treat-
ment. The intervention reduces
excess prevalence for both dis-
eases, but its impact is stronger
on malaria. (B) Impact of reducing
sexual activity during clinical ma-
laria and HIV dual infection as
expressed in HIV and malaria
prevalences in the presence of interaction and activity reduction as compared to the baseline with no interaction and no reduction. The intervention reduces
excess prevalence for both diseases but its impact is more substantial to the HIV epidemic.
8 DECEMBER 2006VOL 314
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